i

EPA/600/R-00/023

July 2000

An International Workshop on Life Cycle

Impact Assessment Sophistication

Co-Organized by:

Jane C. Bare

U.S. Environmental Protection Agency (EPA)

Helias A. Udo de Haes

Centre of Environmental Science (CML)

David W. Pennington

U.S. EPA Oak Ridge Institute for Science and Education (ORISE) Fellow

in co-operation with United Nations Environment Programme (UNEP)

National Risk Management Research Laboratory

Office of Research and Development

U.S. Environmental Protection Agency

Cincinnati, Ohio 45268
ii

Notice

The views expressed in these Proceedings are those of the individual authors and do not necessarily reflect the views and policies of the U.S. Environmental Protection Agency (EPA). Scientists in

EPA’s Office of Research and Development have prepared the EPA sections, and those sections

have been reviewed in accordance with EPA’s peer and administrative review policies and approved

for presentation and publication.
iii

Foreword

The U.S. Environmental Protection Agency is charged by Congress with protecting the Nation’s

land, air, and water resources. Under a mandate of national environmental laws, the Agency strives to

formulate and implement actions leading to a compatible balance between human activities and the

ability of natural systems to support and nurture life. To meet this mandate, EPA’s research program

is providing data and technical support for solving environmental problems today and building a science knowledge base necessary to manage our ecological resources wisely, understand how pollutants affect our health, and prevent or reduce environmental risks in the future.

The National Risk Management Research Laboratory is the Agency’s center for investigation of

technological and management approaches for reducing risks from threats to human health and the

environment. The focus of the Laboratory’s research program is on methods for the prevention and

control of pollution to air, land, water and subsurface resources; protection of water quality in public

water systems; remediation of contaminated sites and ground water; and prevention and control of

indoor air pollution. The goal of this research effort is to catalyze development and implementation of

innovative, cost-effective environmental technologies; develop scientific and engineering information

needed by EPA to support regulatory and policy decisions; and provide technical support and information transfer to ensure effective implementation of environmental regulations and strategies.

This publication has been produced as part of the Laboratory’s strategic long-term research plan.

It is published and made available by EPA’s Office of Research and Development to assist the user

community and to link researchers with their clients.

E. Timothy Oppelt, Director

National Risk Management Research Laboratory
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Preface

This is the report of the second workshop on a topic in the field of Life Cycle Assessment organized

under the auspices of UNEP. The first one was held in June 1998 in San Francisco under the title

“Towards a Global Use of LCA”. An important characteristic of that workshop was the strong input

from experts of developing countries.

The second workshop was in Brussels in November 1998, and focused on the sophistication of Life

Cycle Impact Assessment. Since its coming out in the early seventies, the focus of LCA has been on

the Inventory Analysis. Life Cycle Impact Assessment, or LCIA, was for a long time regarded “to be in

an early stage of development”. This changed with two SETAC working groups, both in North America

and Europe, and with the unifying guidance of the ISO process. In the coming ISO standard on LCIA

only the framework of LCIA will be defined. This still allows for very diverging types of analysis that all

can claim to be ISO compatible. In itself there is nothing wrong with that, but a second SETAC-Europe

working group on LCIAhas begun the process of providing more guidance in this area.

An important issue in this field regards the present topic, the appropriate level of sophistication of

LCIA. As will be clear from the present report, this involves quite a number of issues. A major point

concerns the extension of the characterization modeling to include also the fate of the substances

and not only their effects. Another issue concerns a possible differentiation in space and time. Studies

can include impact models that use data just at world level and do not specify time periods; in

contrast, more recent options involve spatial differentiation of impacts and distinguish between different time periods. A further point concerns the type of modeling. Traditionally LCIA uses linear modeling, like in the Inventory Analysis; but more sophisticated possibilities arise which take background

levels of substances into account and make use of nonlinear dose-response functions. An important

question here is whether there are real science based thresholds, or whether these thresholds are

always of a political origin. A further question relates to the role and practicality of including uncertainty analysis. Sensitivity analysis is increasingly included in LCA studies; but this is not yet the

case for uncertainty analysis. Finally, there is the question of how to apply these different options for

sophistication of LCIA; which applications can afford to keep it simple, and for which applications is a

more detailed analysis needed?

Like with the first workshop in this series, the partners in the process were the US EPA and Centre

of Environmental Science (CML). We can expect that it will not be the last one. An issue that could

only be touched upon indirectly during the second workshop concerns the choice of the category

indicator in the environmental mechanism of an impact category. Should it be chosen at midpoint

level, as is done traditionally in many categories, or should the modeling proceed up to the level of the

category endpoints? We expect that this will be a topic for a third workshop in this series, to be

organized in May 2000 in Brighton.
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Table of Contents

Pre-Workshop Summary.............................................................................................................. vi

Summary of Technical Issues Discussed ....................................................................................1

Agenda...........................................................................................................................................9

Remarks by Co-Chairs

Life Cycle Impact Assessment Sophistication (Jane Bare, U.S. EPA, USA)

LCIA Sophistication Issues Overview (David Pennington, ORISE Fellow, U.S. EPA, USA)

Bio of Helias Udo de Haes, CML, The Netherlands

Determination of Sophistication

ISO Standards: Life Cycle Assessment & Comparative Assertions .............................................. 17

(J. Willie Owens, Procter & Gamble, USA)

Impact Assessment for Ecodesign ............................................................................................... 25

(Mark Goedkoop, Pré Consultants, The Netherlands)

Application Dependency of LCIA ................................................................................................... 31

(Henrik Wenzel, Technical University of Denmark)

Impact Categories & Uncertainty Analysis within Human Toxicity & Ecotoxicity

Types of Uncertainty in the Human Toxicity Potential .................................................................... 35

(Edgar Hertwich, University of California, Berkeley, USA)

The Different Levels of Uncertainty Assessment in LCIA; The Case of Carcinogenic

Effects .......................................................................................................................................... 39

(Patrick Hofstetter, Environmental Sciences: Natural and Social Science Interface, ETH Zurich)

Human Toxicity and Ecotoxicity - Modeling versus Scoring ........................................................... 46

(Olivier Jolliet, EPFL, Switzerland)

Priority Assessment of Toxic Substances in LCA—A Probabilistic Approach................................ 54

(Mark Huijbregts, IVAM, Netherlands)

Acidification, Eutrophication, and Tropospheric Ozone

Implications of Inventory Structure for Life Cycle Impact Assessment and Uncertainty

Analysis ........................................................................................................................................ 61

(Gregory A. Norris, Sylvatica, Maine, USA)

Levels of Sophistication in Life Cycle Impact Assessment of Acidification ................................... 67

(José Potting and Michael Hauschild, Inst. for Product Development, Technical U. of Denmark)

Eutrophication — Aquatic and Terrestrial — State of the Art ......................................................... 77

(Göran Finnveden, fms and Stockholm University)

Appendix ..................................................................................................................................... 83
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Pre-Workshop Summary

Jane Bare, Helias Udo de Haes, and David Pennington

In meetings and journals worldwide, practitioners have

debated the utility of conducting Life Cycle Assessments

(LCAs). The debate has often hinged on the appropriate level

of sophistication. While some have advocated abandoning

LCA altogether, since it is not achievable in its most sophisticated form, others have supported the concept of conducting LCAs while making the limitations and uncertainties transparent.

This proposed workshop is needed to address the appropriate level of sophistication in Life Cycle Impact Assessment (LCIA). The workshop will be formulated to help evaluate available approaches in terms of framework, scope,

impact assessment methodologies and uncertainty analysis. Additionally this workshop will serve to educate potential practitioners in developing countries about issues confronting LCIA, and to include developing countries in small

working group discussions. A number of prominent practitioners and researchers will be invited to present a critical

review of the associated factors, including the current limitations of available impact methodologies and a comparison of the alternatives in the context of uncertainty. Each

set of presentations will be followed by a discussion session to encourage an international discourse with a view to

improving the understanding of these crucial issues. Discussions will be focused around small working groups of

LCA practitioners and experts including representatives from

industry, government, and academia.

Introduction

A UNEP Workshop titled Towards Global Use of LCA was

held on June 12 - 13, 1998 in San Francisco. The purpose

of this workshop was to develop recommendations and an

action plan that would lead toward greater use of LCA in the

context of sustainable development. Fifty LCA users and

experts from 26 different countries attended the workshop.

At the end of the workshop, each of the participants was

asked what actions could lead to greater development and

use of LCA for sustainable development decision making.

One of the many ideas discussed included providing a forum for an International discussion of the appropriate levels

of sophistication within LCIA.

Practitioners of LCA are faced with the task of trying to

determine the appropriate level of sophistication to provide

a sufficiently comprehensive approach to assist in environmental decision making. The impact assessment stage of

LCA, termed LCIA, helps decision-makers to interpret inventory data in the context of a number of impact categories.

In an ideal world, an LCIA would be based on high quality

data. All impact categories and all steps in each causeeffect chain would be considered using state-of-the-art methods, which include fate and account for spatial variation. In

this ideal world, decisions could be made based on these

environmental assessments with a high level of certainty.

In the real world, practitioners have had to deal with the

questions of inaccuracy and uncertainty in LCA and LCIA

in particular. Hence, many simplifications are made within

the LCIA stage. Simplifications may include: reduction in

spatial discrimination (or ignoring spatial variation altogether),

ignoring fate, ignoring background levels of pollutants, assuming linear dose-response curves, and/or eliminating an

impact category altogether because the appropriate assessment methodologies do not exist.

While ideally an impact assessment should be sophisticated in all dimensions, obviously, high levels of sophistication require exhaustive time, data, and resources, or cannot be reached at all. Hence, the scope of the assessment

needs to be defined, possibly iteratively, to provide the appropriate level of sophistication. Defining this scope appropriately is key to effective environmental decision making.

Conducting an uncertainty analysis can be one approach

to determining the appropriate level of sophistication. Unfortunately, many of the studies conducted in the past were

deterministic and therefore, may have misled commissioners into believing that decisions could be based on the studies, when in reality, the compared options were essentially

non-differentiable. This workshop will explore the sophistication of LCIAs including the discussion of uncertainty analysis involved.

Factors Involved in the Determination of

Sophistication

The important issue of deciding the appropriate level of

sophistication is not typically addressed implicitly in LCA.

Often, the determination of sophistication is based on con-
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siderations which may, or may not, be appropriate, but which

may include practical reasons for limiting sophistication (e.g.,

the level of funding). A discussion of the most appropriate

ways of determining sophistication will include:

• project objective

• an uncertainty and/or sensitivity analysis

• the inventory data and availability of accompanying parameters

• depth of knowledge and comprehension in each impact

category

• perceived value placed on the specific impact categories

• the anticipated level of impact

• the quality and availability of modeling data

• available supporting software

• the level of funding

Hope for the Future

There have been a number of advances made in LCIA in

recent times.

• The framework for LCIA is becoming standardized, enhancing the comparability and avoiding unnecessary

variation between studies.

• The fate of substances is increasingly taken into account, in particular using multimedia modeling as a basis

for characterization.

• The results of different characterization procedures for

the same category are compared amongst each other

and they show convergence.

• Better distinctions are being made between scientific

information and value choices.

These developments are leading to advances in the practice of LCIA, but the major limitations and uncertainties cannot be expected to go away in the near future. Instead, practitioners must learn how to best address the concerns and

limitations of the methodologies. Among the questions that

remain, the following list will be tentatively posed within small

group discussions:

Determination of Sophistication

Methods for

What are the most common methods by which the level of

sophistication is determined?

Which methods are considered more acceptable? Why?

What are the barriers to using the acceptable methods?

What can be done to overcome these barriers?

Various Uses of LCIA

Is LCIA application dependent? Why or why not?

What are the expectations in sophistication for the various LCA uses (e.g., government, industry/public communication, and internal use)?

When should LCIAs be as detailed as possible, because

every increase of detail increases accuracy and removes

uncertainty? And when is it better to limit the scope of LCA

to addressing questions on a macroscopic scale, leaving

spatial and threshold consideration to other analytical tools?

How do practitioners determine the value of the trade-offs

necessary when sophistication and comprehensiveness are

“at odds” (e.g., choosing a modeling approach may limit the

number of pollutants that can be characterized vs. a scoring approach which may limit the sophistication of the modeling)?

Uncertainty Analysis

What case studies are available using uncertainty analyses within LCIA? And what are the major findings to date

(levels of uncertainty discovered)? When is the uncertainty

determined to be unacceptable?

Specific Impact Categories & Uncertainty

Analysis within LCIA

Human Toxicity and Ecotoxicity

In human toxicity and ecotoxicity, when is spatial and/or

temporal differentiation necessary? If necessary, what spatial and/or temporal details are recommended (e.g., indoor/

outdoor, and height of emission point)?

In ecotoxicity what is the best approach to addressing

multiple species? If suggested, what are the recommended

representative species?

In human toxicity and ecotoxicity, what are the greatest

barriers to conducting uncertainty analysis?

What are the recommendations for research and/or development in these impact categories?

Eutrophication, Acidification, and

Tropospheric Ozone Formation

In each category, when is spatial and temporal differentiation necessary? If so, what spatial and temporal scales

are recommended (e.g., regionally, or differentiating terrestrial, sweet water and marine)?

In each category, what are the greatest barriers to conducting uncertainty analysis? What are the recommendations for research and/or development in these impact categories?
1

Abstract

On November 29 - 30, 1998 in Brussels, an international

workshop was held to discuss Life Cycle Impact Assessment (LCIA) Sophistication. Approximately 50 LCA experts

attended the workshop from North America, Europe, and

Asia. Prominent practitioners and researchers were invited

to present a critical review of the associated factors, including the current limitations of available impact assessment

methodologies and a comparison of the alternatives in the

context of uncertainty. Each set of presentations, organized

into three sessions, was followed by a discussion session

to encourage international discourse with a view to improving the understanding of these crucial issues. The discussions were focused around small working groups of LCA

practitioners and researchers, selected to include a balance

of representatives from industry, government and academia.

This workshop provided the first opportunity for International experts to address the issues related to LCIA Sophistication in an open format. Among the topics addressed

were: 1) the inclusion or exclusion of backgrounds and thresholds in LCIA, 2) the necessity and practicality regarding the

sophistication of the uncertainty analysis, 3) the implications of allowing impact categories to be assessed at “midpoint” vs. at “endpoint” level, 4) the difficulty of assessing

and capturing the comprehensiveness of the environmental

health impact category, 5) the implications of cultural/philosophical views, 6) the meaning of terms like science-based

and environmental relevance in the coming ISO LCIA standard, 7) the dichotomy of striving for consistency while allowing the incorporation of state-of-the-art research, 8) the

role of various types of uncertainty analysis, and 9) the role

of supporting environmental analyses (e.g., risk assessments). Many of these topics addressed the need for increased sophistication in LCIA, but recognized the conflict

this might have in terms of the comprehensiveness and

holistic character of LCA, and LCIA in particular.

Introduction

A UNEP Workshop titled “Towards Global Use of LCA”

was held on June 12 - 13, 1998 in San Francisco. The purpose of the San Francisco workshop was to develop recommendations and an action plan that would lead towards

a greater use of LCA in the context of sustainable development. At the end of the San Francisco workshop, each of

the participants was asked what actions could lead to greater

development and use of LCA in sustainable development

decision making. One of the many ideas suggested was to

provide a forum for an International discussion of the appropriate practice of LCIA. LCIA Sophistication was taken up

as subject of the workshop held in Brussels, which was

attended by approximately 50 LCA practitioners and experts

from various countries.

Practitioners of LCA are faced with the task of trying to

determine the appropriate level of sophistication in order to

provide a sufficiently comprehensive and detailed approach

to assist in environmental decision making. Sophistication

has many dimensions and dependent upon the impact category, may simulate the fate and exposure, effect and temporal and spatial dimensions of the impact. (Udo De Haes,

1999a, Owens, et al., 1997, Udo de Haes, 1996, Fava, et

al., 1993). In the context of the Brussels workshop, sophistication was considered to be the ability of the model to

accurately reflect the potential impact of the stressors, or

in language more consistent with recent ISO publications,

the ability to reflect the environmental mechanism with scientific validity. (ISO, 1999).

The impact assessment phase of LCA, termed LCIA,

helps decision-makers interpret inventory data in the context of a number of impact categories and to bring them into

a more surveyable format. Ideally, an LCIA would be based

on high quality data. All impact categories and processes

in the environmental mechanism of each of these categories would be considered using state-of-the-art techniques,

which would fully account for spatial and temporal variation.

In such an Ideal World, decisions would be made based on

Summary of Technical Issues Discussed

Jane C. Bare1, David W. Pennington2, and Helias A. Udo de Haes3

1 Corresponding author: U.S. Environmental Protection Agency, National Risk Management Research Laboratory, Cincinnati, Ohio 45268, USA. Email:

bare.jane@epa.gov

2 ORISE Research Fellow, U.S. Environmental Protection Agency, National Risk

Management Research Laboratory, Cincinnati, Ohio 45268, USA.

3 Centre of Environmental Science (CML), Leiden University, P.O. Box 9518, NL2300 RA Leiden, Netherlands.
2

these assessments with a high level of confidence and certainty. However, real world practitioners have to deal with

limitations (e.g., budget, and poor quality data) and simplifications are made. Some modifications may include: 1) reduction in spatial and temporal discrimination (or ignoring

these dimensions altogether), 2) ignoring fate, 3) assuming

linear dose-response curves and/or 4) eliminating an impact category because appropriate data or assessment

methodologies do not exist.

While ideally an impact assessment should be sophisticated in all dimensions, this high level of sophistication requires exhaustive time, data, and resources and generally

cannot be reached due to limitations in methodology and

data available. Hence, the scope of the assessment needs

to be defined, possibly iteratively, to provide the appropriate

level of sophistication, including the required level of detail

and accuracy, together with an uncertainty analysis practical for individual studies, and the specification of value

choices within the framework of the LCA. Appropriate definition of this scope, including sophistication, uncertainty

analysis, and comprehensiveness is the key to effective

environmental decision making.

Many practitioners in the past have attempted to evaluate impacts to support broad LCA-based decisions, but have

oversimplified the impact assessment step. Unfortunately,

limitations in simulation sophistication lead to a reduced

ability of the study to answer the questions at hand with a

high degree of certainty. In the absence of accompanying

uncertainty analysis, and validation (which addresses model

uncertainty), many LCAs are conducted at such a low level

of simulation sophistication that they are ineffectual in differentiating the very options they are trying to evaluate.

(Coulon, 1997; Potting, 1997, Udo de Haes, 1996). Workshop participants also discussed the dichotomy of sophistication and comprehensiveness. As an example, very simplistic methods such as relying solely on toxicity data may

allow a larger chemical database set than a more sophisticated approach which would require additional chemical and

physical properties to determine the relative human health

potentials.

More recently, researchers are recognizing the many types

of uncertainty involved in environmental decision making.

Two types of uncertainty discussed at this workshop were

model uncertainty and data uncertainty. Data uncertainty

may be estimated by the propagation of uncertainty and

variability of the input parameters. Model uncertainty can

only be characterized by comparison of the model prediction with the actual response of the system being addressed.

As data uncertainty is relatively easy to characterize,

whereas model uncertainty is difficult, especially in a field

like LCIA, the presentation of data uncertainty alone may

not appropriately be used to compare two methodologies.

For example, a simplistic approach utilizing only persistency,

bioaccumulation, and toxicity data may appear to be more

certain when compared in terms of data uncertainty to a

more complex multimedia/human exposure approach, but

the unaddressed model uncertainty may significantly overshadow the data uncertainty.

The specification of value choices has a bearing on the

level of sophistication and has been the subject of many

recent papers. (Owens, 1998, Finnveden, 1997, Volkwein,

1996a, Volkwein, 1996b, Powell, 1996, and Grahl, 1996).

Some practitioners are uncomfortable with the subjectivity

of the Valuation Process, but fail to recognize the role of

subjectivity in other phases of the LCA framework. All LCAs

are conducted under the influence of subjective decisions.

In fact, subjective decisions, value choices, or scientific or

engineering judgements are made throughout the LCA process. Thus, the selection, aggregation, or disaggregation of

impact categories and the determination of the methodologies to quantify the potential impacts are all influenced by

value choices. The Brussels workshop was chosen to explicitly address the incorporation of value choices within

the LCA process.

Unfortunately, the important issues of deciding the appropriate level of sophistication often remain unaddressed

in LCIA. The determination of the level of sophistication is

often not based on sound and explicit considerations, but

on practical reasons (e.g. the level of funding, level of inhouse knowledge). The workshop was therefore formulated

to allow a more explicit discussion of the many factors outlined above that can influence the choice of the level of

sophistication of a study, including:

• The project objective

• The perceived value placed on the specific impact categories

• The availability of inventory data and accompanying

parameters

• The depth of knowledge and comprehension in each

impact category

• The quality and availability of modeling data

• The uncertainty and sensitivity analyses

• The level of validations

• The available supporting software

• The level of funding

This paper provides a summary of the results of this workshop, including discussion on many of the above topics. An

attempt is made to provide short reviews of the presentations and discussions. However, in documenting the workshop it was not possible to capture the full detail of the

many points raised.

Workshop Logistics

On the 29th and 30th of November, 1998 in Brussels,

Belgium an international workshop was held to discuss Life

Cycle Impact Assessment (LCIA) Sophistication. Approximately 50 LCA experts attended the workshop, coming from

Europe, Asia and the USA. Several prominent practitioners

and researchers were invited to present a critical review of
3

the associated factors, including the current limitations of

available impact methodologies and a comparison of the

alternatives in the context of uncertainty. Each set of presentations, organized into three sessions, was followed by

a discussion session to encourage international discourse

with the aim to improve the understanding of these crucial

issues. The discussions were focused around small working groups of LCA practitioners and researchers, deliberately selected to include a balance of representatives from

industry, government and academia. Each group was given

the charge to address the questions that most interested

them, as opposed to assigning specific groups with specific questions.

Introductory Session

Jane Bare of the U.S. Environmental Protection Agency

(EPA) opened the workshop noting that many of the participants had been involved in previous meetings as LCIA experts, sometimes even discussing related issues in the

development of ISO 14000 series and SETAC Working

Groups on LCA and LCIA. Requirements are being developed under ISO 14042 to specify a high level of sophistication for Comparative Assertions, including language concerning the scientific validity, environmental relevance, and

the role of value choices. Within SETAC-Europe efforts are

on going to develop a document related to the selection of

the “state-of-the-art” impact assessment methodologies.

Bare asked that participants consider the present workshop

as a more open format than either of these settings to allow

a completely uninhibited technical exchange. She stressed

that Life Cycle Impact Assessment can be effective in supporting environmental decision making, but only if the data

and methods are sufficiently scientifically defensible. Scientifically defensible was defined as being dependent upon

the level of sophistication, the level of certainty (including

both data and model certainty), the level of comprehensiveness, and data availability. The participants were challenged

to address several additional questions throughout the two

days of discussions including: What is “scientifically defensible”? In the sphere of determining whether impact assessment is based on sound science, where does one draw the

line between sound science and modeling assumptions?

Garrette Clark from the United Nations Environment

Programme (UNEP) then provided a short history of UNEP’s

involvement in the area of LCA, which includes providing

technical assistance to developing countries and the development of an associated guidance document for LCA (UNEP,

1996). She stated that LCA is considered by UNEP to be an

important tool for achieving cleaner production and consumption. She also summarized findings from the recent LCA

workshop in San Francisco in June 1998 (UNEP, 1998).

David Pennington discussed two extremes of LCIA sophistication. One extreme he called the “Contribution or

Burden” approach, which is comparable to what has been

historically used in LCIA (reflecting the Precautionary Principle and the combinatory potential to cause impacts). The

other extreme, the “Consecutive Risk Assessment” approach, he noted as being particularly recommendable for

use in areas with high stakes, such as comparative assertions, but as often limited to the assessment of chemicals

in isolation. He introduced the question concerning the need

for spatial differentiation and asked when site-specific differentiation was appropriate. He also pointed out that the

category indicators are chosen at different points in the

environmental mechanism (or cause-effect chain), and

stated that the U.S. EPA has been using the term of “midpoint” to address indicators that stop short of expected effects on the final “endpoint” of the environmental mechanism. He presented acidification as an example of a category with the indicator at “midpoint” level and human health

as a possible example of a category with the indicator at

“endpoint” level. He concluded by asking about the different

levels of sophistication. What is possible? What is required?

When to use the various levels of sophistication?

Session One: Overview

Willie Owens of Procter and Gamble spoke about comparative assertions (i.e., public comparisons between product systems) and the requirements for LCIA under ISO

14042. He stated that ISO 14042 requires a sufficiently

comprehensive set of category indicators, a comparison

conducted indicator by indicator (i.e. no weighting) and that

LCIAs should not be the sole basis for comparative assertions. Current language in ISO 14042 states that subjective

scores, such as weighting across categories, shall not be

used for comparative assertions; that category indicators

be scientifically defensible and environmentally relevant and

that sensitivity and uncertainty analyses shall be conducted.

Mark Goedkoop of Pré Consultants discussed LCIA for

ecodesign. He pointed out that the point of conducting an

LCA is typically to determine whether A is better than B. He

then presented three problems with LCA and ecodesign: 1)

LCA studies are too time consuming, 2) LCA studies are

hard to interpret, and 3) Designers never become experts,

but remain dependent upon experts. His proposed solution

for these problems was to calculate predefined single scores

for the most commonly used materials and processes, and

to incorporate uncertainty into the modeling. He also discussed the sometimes hidden role of societal values in

characterization modeling, even for internationally agreed

models. As an example, he presented the three classes of

carcinogens (proven, probable and possible) and pointed

out that the practitioner must make a decision about whether

to include one, two, or all three classes. He proposed that a

single truth does not exist and that modeling is dependent

upon the chosen perspective. He then introduced three different views of the world based on values: egalitarian, hierarchical and individualist. (A topic discussed later in more

detail by Patrick Hofstetter.) He pointed out that if A is not

better than B in all three cases then the result is dependent

upon the perspective.

Henrik Wenzel of the Technical University of Denmark

discussed the application dependency of LCIA. He mentioned several applications including life cycle management,

strategic planning, product development, process design,

green procurement and public purchasing, and marketing.
4

In addition, he discussed three main variables governing

application dependency: the environmental consequence of

the decision (including spatial and temporal scale), the socioeconomic consequence and the decision context. He discussed the application dependency of uncertainty, transparency, documentation and the inclusion of temporal and

spatial resolution. He stated that the need for sophistication

of LCIA is largest in decisions with the highest requirements

for certainty. He also stated that the decision-maker might

impact the choice of normalization and weighting. (Wenzel,

1998)

Helias Udo de Haes wrapped up this first session by providing a summary of some of the key points covered and

challenging the participants to address the questions provided during the small group discussions. Workshop participants were asked to address the following questions and

to provide additional questions to aid discussion.

Determination of Sophistication and Uncertainty Analysis

1. What are the most common methods by which the

level of sophistication is determined?

2. Which methods are considered more acceptable? Why?

3. What are the barriers to using the acceptable methods? What can be done to overcome these barriers?

4. To what extent should LCIA be application dependent?

5. What are the expectations regarding the level of sophistication for the various LCA applications (e.g., by

government, by industry, for public communication, and

for internal use)?

6. When should LCIAs be as detailed as possible, aiming

at the maximum level of accuracy? And when is it

better to limit the scope of LCA to addressing questions on a macroscopic scale, leaving spatial and

threshold considerations to other analytical tools?

7. How do practitioners deal with the trade-offs necessary when sophistication and comprehensiveness are

“at odds” (e.g., choosing a detailed modeling approach

that may limit the comprehensiveness vs. a scoring

approach that may limit the sophistication)?

8. What case studies are available using uncertainty

analyses within LCIA? And what are the major findings to date (levels of uncertainty discovered)? When

is the uncertainty determined to be unacceptable?

Questions Added at Workshop

9. What is scientifically and technically valid, as included

in the requirements of ISO 14042?

10. If LCIA is an iterative process, what drives the decision on the level of sophistication (e.g., uncertainty

analysis, relevance, and existence of trade-offs)?

11. Define uncertainty in the context of LCIA. What parameters must be analyzed?

12. How do we incorporate background levels into LCIA?

Should we define working points (as in Mark

Goedkoop’s presentation)? Should this be done for

individual chemicals or combined?

13. What is the best currently available method to represent the combined effect of chemicals without double

counting, or inappropriately allocating?

14. How do we incorporate (or should we incorporate) the

differing philosophical views in characterization?

First Session Discussion Summary

An aggregation of the resultant views is presented below:

Determination of Sophistication – Many different groups

commented on the appropriate level of impact assessment

sophistication. One group commented that some sound

decisions may be/have been made on the basis of LCA

studies, which did not have very sophisticated LCIAs, but

these tended to be more obvious cases. They recommended

using the most sophisticated impact assessment models

that provide information closest to the endpoint. Another

group commented that sophistication is dependent upon a

number of things including: inventory data availability, the

availability of characterization models and data to support

these models, objective, the application dependency, the

decision maker’s sphere of influence and the impact category. A third group stated that the choice of sophistication

depends upon an iterative process, where the iterations may

be dependent upon uncertainty, the environmental relevance

of the results and the minimum level of certainty required to

support a decision. Several participants commented that

sophistication is often limited by budget, inventory data

availability, ease of use of impact assessment methods

and in-house knowledge.

These participants stressed the practical side of LCA and

recognized the difficulty in data collection and the structuring of public databases to support more sophisticated analyses.

Application Dependency - There was a general belief that

LCIA sophistication is application dependent, according to

the type of application and not the individual user. For example, screening level LCA studies may not require the rigorous use of sophisticated impact assessment techniques

but final comparative assertions may require much more

rigor, particularly if the benefits are not apparent. LCIA studies should be performed based on the type of question or

decision at hand and the purposes that the LCIA may be

serving.

Validating the Results of LCIAs – There was agreement

that one cannot validate the results of a single LCIA study,

because of the lack of temporal and spatial specification

associated with the inventory data, and an inability to accurately model complex interactions in the environment, in-
5

cluding the combinatory effects of chemical mixtures. However, input data can be quality checked, and elements in

the models can be compared with models developed in the

context of other applications such as environmental risk

assessment. It was also noted that validation might not be

as important in the context of LCIA since models simply

reflect a relative comparison as opposed to an absolute

assessment.

Backgrounds and Thresholds - Practitioners have tried to

incorporate background levels in LCA studies in the past

but there was a lot of discussion that this practice may or

may not be appropriate. One of the questions at hand is

whether emissions do occur in above or below threshold

situations. Another issue concerned the fear that defining

backgrounds and thresholds will lead to treating many environments as infinite sinks (e.g., for acidic chemicals) when

in reality nature’s ability to absorb the impact may be exceeded at some future time. The distinction was also made

that thresholds may be less strict, because of the presence

of very sensitive species or human individuals. Thresholds

may also not be protective enough in many environments in

which the combined effects of chemicals may cause effects at a level much lower than the threshold effect. Finally, practitioners were cautioned not to use LCIA to the

exclusion of recognizing the problem of hot spots surrounding facilities. (See the following point for more information

on mixtures). On the other hand, some participants believed

that thresholds might be valuable indicators of relative potency for many chemicals and that thresholds had been

derived with statistically sound methods. Further clarification of the decision-making context may be necessary to

determine the value of thresholds and backgrounds in particular applications of LCIA.

Mixtures - One of the basic limitations of the current stateof-the-science of LCIA of human and ecotoxicity is the inability to effectively deal with potential combinatory effects

of chemical mixtures. Toxicologists operate under the assumption that chemicals acting on the same organ can be

considered to have an additive effect, but often LCIA impact categories are much broader than a focus on target

organs. Therefore, the same assumptions used in risk assessment are not applicable to LCIA. This is especially an

issue when practitioners try to incorporate threshold levels

for individual chemicals into LCIA. Because mixtures are

not well characterized in LCIA, effects may be occurring at

much lower levels than the accepted threshold levels of the

individual chemicals. Practitioners often try to compensate

for these and other model deficiencies by adopting the Precautionary Principle.

Data Gaps – There was a concern that data gaps can be

significant. Particularly in human and ecotoxicity, availability and quality of both inventory and chemical data to support the modeling of a large number of chemicals can be

frustrating. These impact categories are a good example of

where less sophisticated screening techniques may, with

an appropriate degree of caution prove useful.

Uncertainty Analysis - LCIA still faces great challenges

before fully addressing uncertainty analysis. Some of these

challenges include the lack of awareness, lack of associated methodology, and the perceived difficulty of presenting the results to decision-makers. Specifically, practitioners need better knowledge of uncertainties in existing

methods within the different impact categories and of the

potential for improvement, if any, by using methods with

greater sophistication. Many participants acknowledged a

need for a better understanding of the uncertainty involved

in each of the impact assessment methodologies for each

of the impact categories, noting that uncertainty is associated with the models as well as the input data. The potential

trade-off in available models between increased sophistication (i.e., detail) and reduced comprehensiveness (e.g.,

number of stressors simulated) was again noted.

Unnecessary Rigor? – There was a belief that the ISO

standard on LCIA, specifically for the comparative assertions to be disclosed to the public, is too demanding in the

areas of scientific validity and certainty. Examples were given

of some other modeling arenas that face the same challenges (e.g., economic modeling, risk assessment studies). In these fields large uncertainties are accepted, expected and (sometimes) clearly documented. There was also

a concern that the rigor expected of the impact categories

without a working international acceptance (e.g., human

toxicity) exceeds the rigor and certainty requirements compared with the impact categories that benefit from having

international consensus (e.g., global warming potentials).

Model uncertainty vs. data uncertainty – Some participants commented that the current disparity in levels of uncertainty analysis may have led to the false impression that

the more sophisticated models have increased uncertainty

when compared to less sophisticated techniques. Typically

this is not the case. Usually, with a more sophisticated model

the model uncertainty has decreased and the ability to model

data certainty quantitatively has increased. Deceptively

(since model uncertainty is not typically characterized) the

increased characterization of data certainty may have

seemed to increase total uncertainty. (Additional details on

uncertainty analysis may be found in Edgar Hertwich’s presentations.)

Standardization – While it was recognized that the level

of sophistication might depend upon the type of application

and the availability of data, there was a belief that consistency of approach or methodology may be an important

priority to allow comparability between studies. Some participants pointed out that certain studies may only require

Life Cycle Thinking and therefore, should not be subject to

the standardized methodologies. Others addressed the idea

of approach hierarchies that differentiate between screening and more intensive techniques but noted that the approaches could be consistent within these tiers. It was similarly noted that there could be a trade-off between sophistication and comprehensiveness, while one approach provides a more complete picture but with low level of detail,

another may provide a higher level of detail but at the expense of comprehensiveness. It was further noted that there

is continual development of methods and standardization

should not discourage further research efforts.
6

More Focused Research - More energy needs to be expended to ensure that LCA research is focused on areas

that will have the greatest impact. Research needs to be

conducted in deriving better methodologies for more relevant

indicators. Specifically, land use, habitat alteration, and

environmental toxicity were mentioned as examples of impact categories requiring much more research.

Session Two: Human Health and Ecotoxicity

Edgar Hertwich of the University of California, Berkeley

opened the session on Human and Ecotoxicity with his presentation: “A Framework for the Uncertainty Analysis of the

Human Toxicity Potential”. He presented the purpose of

uncertainty analysis: “to develop confidence in an analytical result, as an input to formal decision analysis techniques

and as a tool to refine impact assessment methods.” He

noted that uncertainty analysis includes: parameter uncertainty, model uncertainty, decision rule uncertainty and variability. He then presented various examples of each of these

as they might pertain to modeling for human toxicity impact

assessment in LCIA. Finally, he pointed out that simply

conducting a sensitivity analysis can often provide valuable insights about the significance of the multiple uncertainties involved in the decision and can help refine impact

assessment techniques. (Hertwich, et al., 1993; Hertwich,

1999)

Patrick Hofstetter of the Swiss Federal Institute of Technology in Zurich addressed the question of “What is science?” in the presentation: “The Different Levels of Uncertainty Assessment in LCIA: The Case of Carcinogenic Effects.” He stated that the development of models is dependent on the perspective of the modeler. Three perspectives

were described: hierarchist, individualist and egalitarian. An

individualist optimizes the spending of resources based upon

the known or certain types of harm that can be modeled

(e.g., only choosing to include IARC Group 1 Carcinogenics

in an analysis). A hierarchist could be closest to the operating positions typically held by government and international

organizations and would include Group 1 and Group 2 Carcinogens. Egalitarians tend to take a more risk aversive

and preventive standpoint and thus would include Groups

1, 2, and 3 in a carcinogenic analysis. Similarly, these different perspectives would derive different discount rates for

use within an assessment in terms of the Disability Adjusted Life Years (DALY). An illustration showed the combination of the assumptions of all three cultural perspectives

in an eco-index probability graph. Finally, he concluded that

LCIA could be made simple to use and yet robust by incorporating the values associated with various perspectives

and allowing an analysis of the related technical, methodological and epistemological uncertainties. (Hofstetter, 1998)

Olivier Jolliet of the Swiss Federal Institute of Technology in Lausanne discussed “Human Toxicity and Ecotoxicity

Modeling vs. Scoring.” He opened by saying “Tell me your

results and I will tell you who paid you!” Then he called for

the identification of best available practice regarding impact

assessment methods to reduce the ability to provide LCAs

that support such malpractice. He also proposed that this

process should try to meet the ISO 14042 requirements to

be “scientifically and technically valid” and “environmentally

relevant.” After comparing different human toxicity modeling efforts, he pointed out parameters and model characteristics that are important in human and ecotoxicity modeling, including exposure and fate uncertainties, that can be

responsible for significant uncertainty and which open options for reduction of modeling uncertainty by proper empirical or experimental validation. He concluded by saying

that modeling comparisons should be made based on model

characteristics and consistent data.

Mark Huijbregts of the University of Amsterdam presented

a paper on “Priority Assessment of Toxic Substances in

LCA: A Probabilistic Approach.” Citing previous publications

(e.g., Guinée, et al., 1996 and Hertwich, et al., 1998), he

suggested that the following specific improvements are

needed: a review of default values with the possibility of

using more realistic values, an inclusion of all relevant environmental compartments and inclusion of a Monte Carlo

type of uncertainty analysis. He presented a probabilistic

simulation of weighted human, aquatic and terrestrial Risk

Characterization Ratios (RCRs) for 1,4-dichlorobenzene and

2,3,7,8-TCDD and demonstrated that only a few substancespecific parameters are responsible for the uncertainty in

results. Finally, Huijbregts concluded that variability is not

of significance if it is identical for all options being compared and asked that researchers continue to explore the

issue of when data uncertainty/variability cancel in relative

comparison applications.

Second Session Discussion Summary

Workshop participants were asked to address the following questions and to provide additional questions to aid discussion.

1. In human toxicity and ecotoxicity, when is spatial and/

or temporal differentiation necessary? If necessary,

what spatial and/or temporal details are recommended

(e.g. indoor/outdoor, height of emission point)?

2. With respect to ecotoxicity what is the best approach

to addressing multiple species? If suggested, what

are recommended representative species?

3. With respect to human toxicity and ecotoxicity, what

are the greatest barriers to conducting uncertainty

analysis?

4. What are recommendations for research and development in these impact categories?

An aggregation of the groups’ views is presented below:

Standardization – Again the question of standardization

was discussed. Specifically, if the practitioner or study commissioner can have such a strong influence on the final

results of the study, then perhaps some standardization

would be useful to provide comparability between studies.

However, what perspective or aggregation of perspectives

should be represented in a standardized approach? Should
7

central tendency assumptions or worst-case assumptions

be used? Some participants stated that additional time was

needed to ferment an opinion in this area. Others contended

that “allowing” for too many methods and approaches could

undermine the credibility of LCIA. However, many believed

that now is the time to capture the state-of-the art in a document, while still allowing room for advances in the future.

Several participants expressed interest in being involved in

the current SETAC-Europe Working Group on Life Cycle

Impact Assessment. (Udo de Haes, et al, 1999a and Udo

de Haes, et al, 1999b).

Midpoint vs. Endpoint Level – In further discussion of the

concepts of midpoints vs. endpoints, many participants discussed the advantages of making all impact assessment

models as close as possible to the final endpoints of the

environmental mechanism of the impact categories (e.g.,

quantifying fish kills and trees lost as opposed to the acidification potential of the substances). One benefit of this

approach would be to allow more common endpoints for the

valuation process, perhaps even opening the door to allowing more economic valuation of endpoints. Others pointed

out that this might be unnecessary in a relative comparison

context. They stated that extending the models to the endpoints will narrow down the comprehensiveness of the impacts considered, and will include many more assumptions

and value judgements into the assessment. This may subsequently increase the uncertainty of the results and reduce credibility by further mixing “science and value judgements.”

Ecotoxicity – There was a strong call for research in this

area. There was a recognized need to extrapolate ecotoxicity

in a manner similar to human toxicity with representative

species but also a realization that representative species

may vary within different areas. However, there was also

some discussion that LCA is a very macroscopic tool and,

can not be expected to accurately model local issues. Perhaps, ecotoxicity is so specific to the locality affected that

an attempt should not even be made to include it as an

impact category. The most widely held view on this topic

seemed to be that ecotoxicity should continue to be included,

for the sake of providing a more holistic picture, and that

the potential for more site-specific approaches should be

considered further.

Potentially Affected Fraction of Species (PAFs) – Mark

Goedkoop gave an impromptu presentation on PAFs. He

stated that PAFs are different from PNECs in that they take

the background level of the substances into account and

thus enable nonlinear modeling of impact on the species

composition. Many in principle liked the idea of PAFs and

combined PAFs that represent the combined effect of chemicals. However, there were concerns related to the possibility of identifying PAFs, due to the limited availability of dose

response curves and of background concentration data for

so many chemicals. A discussion of Eco-Indicator 98’s relationship to PAFs was held. (Goedkoop, 1998)

Borrowing from Risk Assessment - Concern was voiced

that LCIA for human toxicity is often based on typical risk

assessment practice (e.g., the use of toxicological benchmarks). Caution was particularly high in the context of deterministic safety factors used in the toxicity component of

the characterization factors, many of which compensate for

low test species numbers. As this reduces the equity and

comparability of chemicals, participants suggested that LCIA

must be careful when adopting deterministic risk assessment perspectives.

Research into Increasing Sophistication and the Role of

Other Assessment Techniques – One group asked for increasing temporal modeling, real ground concentration measurement, incorporation of population density into simulations and better representation of food webs. In this group,

there was a concern that the current direction of research in

multimedia modeling would not address these areas. However this must be viewed in the context of the aims which

are to be met by LCA as opposed to the types of analytical

tools. Thus, another group stressed that perhaps practitioners are too concerned with detail. Perhaps the focus should

remain on macro differentiation of substances in terms of

their persistent, bioaccumulative, and toxic (PBT) properties. This could be subsequently complimented (if required)

by local scale analysis using other tools, and would help to

include a larger set of chemicals at a sufficient level of

differentiation.

Session Three: Acidification, Eutrophication

and Inventory

Greg Norris of Sylvatica, North Berwick, Maine, USA,

presented a “Value-of-Information Approach.” [He pointed

out that uncertainty analysis allows some additional information (e.g., confidence intervals associated with data uncertainty) within the decision-making framework.] Norris

stated that the level of sophistication should be partially

dependent upon the inventory data and its uncertainty, upon

the appropriate models and upon decisions about weighting. He suggested using Input/Output-based upstream LCI

databases to answer many of the common questions that

practitioners face, such as “How many sites, with how much

geographic dispersion, contribute significantly to inventory

totals?” And “What are the expected shapes of these distributions?” He also cautioned participants against trying to

draw conclusions about the preferability of more detailed

LCIA, based on a Probability Density Function (PDF) or

Cumulative Density Function (CDF) diagram, pointing out

that further simulations may be required. Finally, he discussed the difference between analyzing uncertainty in

weighting and in characterization modeling and the need to

treat these issues jointly in the determination of the level of

sophistication and decision support.

José Potting of the Technical University of Denmark presented “Levels of Sophistication in Life Cycle Impact Assessment of Acidification.” Potting presented a case study

comparing alternative locations for copper production and

demonstrated the potential need for site-specific simulations, including emission dispersion and deposition patterns,

background depositions on receiving ecosystems, and the

sensitivity of receiving ecosystems. [She used the Regional
8

Air pollution INformation System (RAINS) model (from

IIASA) with calculations based on Critical Loads provided

by the National Institute of Public Health and the Environment (RIVM) in the Netherlands and transfer-matrices from

EMEP MSC-W at the Norwegian Meteorological Institute.]

She announced that easy-to-use acidification factors had

been established for 44 European regions and suggested

that utilizing this site dependent approach for acidification

resulted in a significant reduction in uncertainty.

Göran Finnveden of Stockholm University presented two

topics - “Eutrophication – Aquatic and Terrestrial – State of

the Art,” and “Thresholds/No Effect Levels/Critical Loads.”

Finnveden discussed the site dependency of eutrophication in three models, developed since 1993. He presented

additional topics for discussion and research related to

eutrophication. In his second presentation, Finnveden proposed that thresholds may, at the macrolevel, have no scientific basis and in fact may just be “acceptable” levels of

risk and thus constitute value choices. Acidification and

human toxicity were used as examples of impact categories that should not ignore “below threshold values.” In line

with this, he proposed that threshold values should not exist in LCIA for any impact category.

The third session was concluded with the large group

documenting some of the earlier topics and discussing the

value of conducting future similar workshops. An on-site

workshop summary was presented by two of the co-chairs.

Conclusions

In meetings and journals world wide, practitioners have

debated the utility of conducting Life Cycle Assessment

studies. The debate has often hinged on the appropriate

level of sophistication. While some have advocated abandoning LCA altogether, since it is not achievable in its most

sophisticated form, others have supported the concept of

conducting LCA studies at a more holistic level, while making the limitations and uncertainties transparent. This workshop discussed many of the issues of dealing with the appropriate level of sophistication in the Life Cycle Impact

Assessment phase of an LCA study.

A number of prominent practitioners and researchers presented a critical review of the associated factors, including

the current limitations of available impact methodologies

and a comparison of alternatives in the context of model

and data uncertainty. On the one hand the workshop addressed the various factors which are connected with an

increase of sophistication in LCIA. Examples include: a)

the need for better fate and effect models, b) the role of

spatial and temporal differentiation, c) the identification of

background levels and thresholds, and d) the need to specify

value-laden aspects such as connected with different cultural perspectives. On the other hand, the holistic and comparative character of LCA was stressed. In this context,

many questioned whether LCA should aim to conduct sophisticated site specific risk assessments, particularly when

this high level of detail may give a false impression of great

confidence, especially when it is not presented with a stringent uncertainty analysis. Moreover, it was recognized that

thresholds reflect value choices about what is regarded

acceptable, rather than science based parameters. And finally, increasing level of detail can increase model certainty,

but, in some cases, may reduce the comprehensiveness.

Workshop speakers and participants discussed the way

that philosophical views may affect not only the valuation

process, but also the impact assessment phase by including assumptions that include values based on the differing

perspectives. This further complicates the question of what

is “science-based” and what are “reasonable” modeling assumptions. Arguments were raised both for and against striving for consistency at this time in the effort to standardize

some of the methods and assumptions to allow comparability between studies.

There was much discussion about the decision-making

framework and the role of other environmental analyses,

such as risk assessment. From the sophisticated uncertainty analyses presented it was obvious that great advances

are being made, but there are many very basic principles

that still lack consensus (e.g., the use of threshold values

and background concentrations). As in risk assessment,

there is great attention to being true to the science, but in

the interest of practicality, a great need for simplifying assumptions.

There was consensus that the workshop was very valuable and that this exchange should be continued through email discussions and periodic workshops (next target workshop in Brighton, U.K. in May 2000). Several topics were

mentioned for future workshops, including: LCIA at strategic levels of decision making (including sustainable development decision support), community planning using LCIAtype indicators, the role of value choices in characterization modeling, and the state-of-the-science for characterizing ecotoxicity in LCIA.

References

Coulon, R., Camobreco, V., Teulon, H., Besnainou, J.,

(1997): Data Quality and Uncertainty in LCI. International Journal of LCA, Vol. 2, No. 3, pp. 178 – 182.

Fava, J., Consoli, F., Denison, R., Dickson, K., Mohin,

T., Vigon, B., (1993): A Conceptual Framework for

Life-Cycle Impact Assessment. Society of Environmental Toxicology and Chemistry: Pensacola, FL.

Finnveden, G., (1997): Valuation Methods Within LCA –

Where are the Values?. International Journal of LCA,

Vol. 2, No. 3, pp. 163 – 169.

Goedkoop, M., Hofstetter, P., Muller-Wenk, R.,

Spriensma, R., (1998): The Eco-Indicator 98 Explained, International Journal of LCA, Vol. 3, pp. 352

– 260.

Grahl, B., and Schmincke, E., (1996): Evaluation and

Decision-making Processes in Life Cycle Assessment. International Journal of LCA, Vol. 1, No. 1, pp.

32 – 35.
9

Hertwich E.G., Pease W.S., and McKone T.E., (1993):

Evaluating Toxic Impact Assessment Methods: What

Works Best? Environmental Science and Toxicology,

32, 138A – 145A.

Hertwich, E. G. (1999) Toxic Equivalency: Accounting for

Human Health in Life-Cycle Impact Assessment. Ph.D.

thesis, Energy and Resources Group, University of California, Berkeley.

Hertwich, E. G., T. E. McKone and W. S. Pease (in press)

“Parameter uncertainty and variability in evaluative fate

and exposure models.” Risk Analysis.

Hofstetter, Patrick, (1998): Perspectives in Life Cycle Impact Assessment: A Structure Approach to Combine

Models of the Technosphere, Ecosphere and

Valuesphere. Kluwer Academic Publishers, Norwell,

Massachusetts.

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DIS 14042: Environmental Management – Life Cycle

Assessment – Life Cycle Impact Assessment.

Owens, J.W., (1998): Life Cycle Impact Assessment: The

Use of Subjective Judgements in Classification and

Characterization. International Journal of LCA, Vol. 3,

No. 1, pp. 43 – 46.

Owens, J.W., Amey, E., Barnthouse, E., Consoli, F., Coulon,

R., Fava, J., Humphreys, K., Haaf, B., Hunt, B.,

Laibson, L., Mones, E., Noesen, S., Norris, G., Todd,

J.A., Vigon, B., Weitz, K., Young, J., (1997): Life-Cycle

Impact Assessment: State of the Art. Society of Environmental Toxicology and Chemistry: Pensacola, FL.

Potting, J., Hauschild, M., (1997): Spatial Differentiation in

Life-Cycle Assessment via the Site-Dependent Characterization of Environmental Impact from Emission.

International Journal of LCA, Vol. 2, No. 4, pp. 209 –

216.

Potting, J., Hauschild, M., Wenzel, H., (1999): “Less is Better” and “Only Above Threshold”: Two Incompatible Paradigms for Human Toxicity in Life Cycle Assessment.

International Journal of LCA, Vol. 4, No. 1, pp. 16 – 24.

Powell, J., Pearce, D.W., Craighill, A.L., (1996): Approaches

to Valuation in LCA Impact Assessment. International

Journal of LCA, Vol. 2, No. 1, pp. 11 – 15.

Udo de Haes, H., Jolliet, O., Finnveden, G., Hauschild, M.,

Krewitt, W., and Muller-Wenk, R., (1999a): Best Available

Practice Regarding Impact Categories and Category Indicators in Life Cycle Impact Assessment – Part 1. International Journal of LCA, Vol. 4, No. 2, pp. 66 – 74.

Udo de Haes, H., Jolliet, O., Finnveden, G., Hauschild, M.,

Krewitt, W., and Muller-Wenk, R., (1999b): Best Available

Practice Regarding Impact Categories and Category Indicators in Life Cycle Impact Assessment – Part 2. International Journal of LCA, Vol. 4, No. 3, pp. 167 – 174.

Udo de Haes, H., (ed.,) (1996): Towards a Methodology for

Life Cycle Impact Assessment, SETAC-Europe Working Group on Impact Assessment, Brussels.

UNEP Industry and Environment (1996): Life Cycle Assessment: What is it and How to Do It. United Nations Publication Sales no. 9C-III-D.2, Paris, France.

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Volkwein, S., Gihr, R., Klopffer, W., (1996a): The Valuation

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Volkwein, S., Gihr, R., Klopffer, W., (1996b): The Valuation

Step Within LCA. Part II: A Formalized Method of

Prioritization by Expert Panels. International Journal of

LCA, Vol. 1, No. 4, pp. 182 – 192.

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the Method. International Journal of LCA, Vol. 3, No. 5,

pp. 281 – 287.

Workshop Agenda

Sunday, Nov. 29

15:00 - 15:45 Introduction of Workshop by Co-Chairs

15:45 - 21:30 Determination of Sophistication

15:45 - 16:15 ISO Standards: LCA & Comparative Assertions (Willie Owens, P&G, USA)

16:15 - 16:45 Impact Assessment for Ecodesign (Mark

Goedkoop, Pré, Netherlands)

16:45 - 17:15 Application Dependency of LCIA (Henrik

Wenzel, Tech U of Denmark)

17:15 - 19:00 Dinner

19:00 - 20:30 Small Group Discussion of Determination of Sophistication

Questions Provided

20:30 - 20:45 Coffee Break

20:45 - 21:30 Small Group Presentations

Monday, Nov. 30

08:00 - 17:30 Specific Impact Categories & Uncertainty Analysis within LCIA

08:00 - 12:00 Human Toxicity & Ecotoxicity

08:00 - 08:30 Types of Uncertainty in the Human Toxicity Potential

(Edgar Hertwich, UC Berkeley, USA)
10

08:30 - 09:00 The Different Levels of Uncertainty Assessment in LCIA; The Case of Carcinogenic Effects (Patrick Hofstetter, Swiss

Federal Institute of Technology)

09:00 - 09:30 Human Toxicity and Ecotoxicity - Modeling vs. Scoring (Olivier Jolliet, EPFL,

Switzerland)

09:30 - 10:00 Priority Assessment of Toxic Substances

in LCA: A Probabilistic Approach (Mark

Huijbregts, IVAM, Netherlands)

10:00 - 11:15 Small Group Discussion of Human Health

and Ecotoxicity

Questions Provided

11:15 - 12:00 Small Group Presentations

12:00 - 13:30 Lunch

13:30 - 16:30 Acidification, Eutrophication, and Tropospheric Ozone

13:30 - 14:00 Implications of Inventory Structure for

LCIA and Uncertainty Analysis (Greg

Norris, Sylvatica)

14:00 - 14:30 Levels of Sophistication in LCIA of Acidification (José Potting, Tech U of Denmark)

14:30 - 15:00 Eutrophication – Aquatic and TerrestrialState of the Art (Göran Finnveden, fms

and Stockholm University)

15:00 - 15:15 Coffee Break

15:15 - 16:45 Large Group Discussion

16:45 - 17:30 Workshop Revelations and Lessons

Learned

Closing Remarks by Co-Chairs

Bare’s Biography

Bare is the Impact Assessment and Measurement (I AM)

Team Leader within the Sustainable Technology Division of

the National Risk Management Research Laboratory. The

role of the I AM Team is to influence environmental decision making in Sustainable Development, Life Cycle Impact Assessment, and Pollution Prevention through the research, development, and application of comprehensive

impact assessment and progress measurement. Bare has

been active in ISO 14042 (Life Cycle Impact Assessment)

providing written and verbal comments to the U.S. position

and representing the U.S. position on several issues. She is

also the U.S. EPA representative and an active member of

the American Institute of Chemical Engineers (AICHE) Center for Waste Reduction Technologies (CWRT) Sustainability Metrics Working Group which is designing a framework

for Sustainable Development decision making within large

U.S. chemical companies, including Monsanto, DuPont,

Rohm and Haas, Eastman Kodak, and others. She has given

numerous U.S. and International presentations related to

Life Cycle Impact Assessment and Sustainable Development. Bare and other members of the I AM team conduct

research into improved impact assessment methodology

development and impact assessment application to support environmental decisions. One tool - TRACI - Tool for

the Reduction and Assessment of Chemical and other environmental Impacts will provide improved impact assessment methodologies for five of seven major chemical impact categories. Bare is a chemical engineer with 15 years

of experience within the U.S. EPA.
11

Bare’s Overheads

Life Cycle Impact Assessment

Sophistication

Organized by:

Jane C. Bare, U.S. EPA ORD

Helias A. Udo de Haes, CML

David W. Pennington, U.S. EPA ORISE Fellow

in co-operation with UNEP

CML

Centre of Environmental Science

Life Cycle Impact Assessment

can be effective in

environmental decision making,

but

only if the data and

simulations support a

scientifically defensible

decision point.

Scientifically Defensible

Decision Point

Dependent Upon:

Level of Sophistication

Level of Uncertainty Analysis

Level of Comprehension

Data Availability

Data Quality

How is LCIA Sophistication

Determined?

To Be Addressed

• project objective or application

state-of-art for the impact category

• uncertainty and/or sensitivity analysis

quality and availability of modeling data

How is LCIA Sophistication

Determined?

Recognized but Not Addressed

perceived value of the impact category

anticipated level of impact

inventory data available

supporting software available

awareness of available models

the level of funding

Impact Category Trends

• Simulations are becoming more

sophisticated as technology advances.

• Researchers are increasingly aware of

developments in other countries and are

testing and comparing methodologies.

• More discussions are addressing the

limitations of LCIA and the difference

between scientific and non-scientific

model simplifications.
12

Your Challenge

To discuss:

current LCIA sophistication, including

temporal and spatial resolution,

major findings to date concerning the

scientific validity of current LCIAs, and

the research needs in this area.

Sunday Afternoon, Nov. 29

15:00 - 15:45 Introduction of Workshop by UNEP & Co-Chairs

15:45 - 21:30 Determination of Sophistication

15:45 - 16:15 Industrial Use: Comparative Assertions

(Willie Owens, P&G)

16:15 - 16:45 Internal Use Only (Mark Goedkoop, Pré Consultants)

16:45 - 17:15 Application Dependency of LCIA -

(Henrik Wenzel, Tech U of Denmark)

17:15 - 17:30 Summary Remarks by Co-Chair

17:30 - 19:00 Dinner

19:00 - 20:30 Small Group - Determination of Sophistication

Questions Provided

20:30 - 20:45 Coffee Break

20:45 - 21:30 Small Group Presentations

Monday Morning, Nov. 30

08:00 - 17:30 Specific Impact Categories & Uncertainty 

Analysis within LCIA

08:00 - 12:00 Human Toxicity & Ecotoxicity

08:00 - 08:30 Discussion of Uncertainty Types & Human Health

Example (Edgar Hertwich, UC Berkeley, USA)

08:30 – 09:00 Structured Approach with Quantitative Uncertainty

Assessment (Patrick Hofstetter, Swiss Fed. Inst. of

Tech.)

09:00 - 09:30 Human Toxicity and Ecotoxicity Modelling vs. Scoring

(Olivier Jolliet, EPFL, Switzerland)

09:30 - 10:00 Uncertainty in Toxicity (Mark Huijbregts, IVAM, 

Netherlands)

10:00 - 11:15 Small Group - Human Health and Ecotoxicity

Questions Provided

11:15 - 12:00 Small Group Presentations

12:00 - 13:30 Lunch

Monday Afternoon, Nov. 30

13:30 - 16:30 Acidification, Eutrophication, and Tropo. Ozone

13:30 - 14:00 Smog Formation Characterization Analysis (Greg

Norris, Sylvatica)

14:00 - 14:30 Levels of Soph. Of Acidification (José Potting, Tech U

of Denmark)

14:30 - 15:00 Eutrophication (Göran Finnveden, fms and Stockholm

University)

15:00 - 15:15 Coffee Break

15:15 - 16:00 Small Group - Three Impact Categories

Questions Provided

16:00 - 16:45 Small Group Presentations

16:45 - 17:30 Workshop Revelations and Lessons Learned

Closing Remarks by Co-Chairs

Determination of Sophistication

Methods for

1. What are the most common methods by which the level of sophistication is

determined?

Which methods are considered more acceptable? Why?

2. What are the barriers to using the acceptable methods? What can be done

to overcome these barriers?

Various Uses of LCIA

3. Is LCIA application dependent? Why or why not?

4. What are the expectations in sophistication for the various LCA uses (e.g.,

government, industry/public communication, internal use)?

5. When should LCIAs be as detailed as possible, because every increase of

detail increases accuracy and removes uncertainty? And when is it better to

limit the scope of LCA to addressing questions on a macroscopic scale, leaving

spatial and threshold consideration to other analytical tools?

6. How do practitioners determine the value of the trade-offs necessary when

sophistication and comprehensiveness are “at odds” (e.g., choosing a modeling

approach may limit the number of pollutants that can be characterized vs. a

scoring approach which may limit the sophistication of the modeling)?

Human Toxicity and Ecotoxicity

Uncertainty Analysis

1. What case studies are available using uncertainty analysis within LCIA? And

what are the major findings to date (levels of uncertainty discovered)? When is

the uncertainty determined to be unacceptable?

Human Toxicity and Ecotoxicity

2. In human toxicity and ecotoxicity, when is spatial and/or temporal

differentiation necessary? If necessary, what spatial and/or temporal details

are recommended (e.g., indoor/outdoor, height of emission point)?

3. In ecotoxicity what is the best approach to addressing multiple species? If

suggested, what are the recommended representative species?

4. In human toxicity and ecotoxicity, what are the greatest barriers to

conducting uncertainty analysis?

5. What are the recommendations for research and/or development in these

impact categories?
13

Eutrophication, Acidification, and

Tropospheric Ozone Formation

Uncertainty Analysis

1. What case studies are available using uncertainty analysis within LCIA? And

what are the major findings to date (levels of uncertainty discovered)? When is

the uncertainty determined to be unacceptable?

Eutrophication, Acidification, and Tropospheric Ozone Formation

2. In each category, when is spatial and temporal differentiation necessary? If

so, what spatial and temporal scales are recommended (e.g., regionally, or

differentiating terrestrial, sweet water and marine)?

3. In each category, what are the greatest barriers to conducting uncertainty

analysis?

4. What are the recommendations for research and/or development in these

impact categories?
14

Pennington’s Biography

Pennington began his career at the University of Surrey in

England, where he obtained an Honors Degree in Chemical

Engineering in 1990. After two years with the consultant company Advanced Mechanics and Engineering (AME),

Pennington accepted a scholarship at the Hong Kong University of Science & Technology to perform research in the Department of Chemical Engineering under the supervision of

department head, Professor P.L. Yue. Completing development

of a software tool and the associated Ph.D. thesis “A Pollution

Prevention Tool for Continuous Chemical Processes (P2TCP)”

in 1997, he then accepted a Post Doctoral Research Fellowship at the Systems Analysis Branch of the US EPA National

Risk Management Research Laboratory (NRMRL) in Cincinnati, Ohio. There, Pennington has continued his research and

development activities in the areas of environmental impact

comparison and screening. A key interest in his R&D activities has been the simplification and use of multimedia models in the application of predicting persistence, relative impact potentials and long-range transport capabilities of chemicals. He is currently a participant in three key activities: the

US EPA Persistent Bioaccumulative and Toxic Pollutants Initiative (PBTI) working group; Jane Bare’s development of Pollution Prevention Progress (P2P) and the Tool for Reduction

and Assessment of Chemical and other environmental Impacts (TRACI); and the SETAC Europe LCA working groups

on chemical fate and exposure, human toxicological impacts

and ecological toxicological impacts.
15

Pennington’s Overheads

Dr. David W. Pennington

ORISE Research Fellow

US EPA Systems Analysis Branch

Cincinnati

“Contributions or Burden” Approach

• Typical LCIA Approach

• Not actual impacts associated with specific emissions

• More than one chemical and one emission

“Consecutive Risk Assessments” Approach

• Comparative assertions (?) and LCSEA

• Actual impacts associated with a specific emissions

• Thresholds ?

What level of subjectivity acceptable ?

Need to distinguish science vs. non-science based?

•Disability Adjusted Life Years

• Average human inhalation rate in models

Subjectivity!

Screening: PBT Scoring Approaches

• Scientifically combined with release quantity ?

• Qualitative ranking (high, medium, low) ?

• More chemicals ?

Continuous scales: Potentials

• Potentials modify release quantities

• Ability to be comprehensive (state-of-the-art) ?

• All pathways, species, interaction of chemicals ...?

Sophistication of Model

Global

•Human & Eco ?

Regional/Remote

•Human & Eco ?

•What boundaries ?

•Advection rates ?

•What Species ?

•Other Fate Parameters ?

•Which Models?

•Acute Impacts?

Local

•Human & Eco ?

•What is local ?

•In LCA ?

•Acute Impacts?

•Which Models?

Spatial Differentiation

Midpoints: Acidification Potential - # of H+ ions

• Simplicity & reflective of “contributions” LCIA approach

• Lower uncertainty ?

• How to measure it ?

• Lack of spatial and temporal variation -Problem ? How ?

Endpoints: Human Health Potentials - fate, exposure and effects

• Complex but not time intensive ?

• Uncertainty and variation quantifiable ?

• High ?

• Can consider spatial and temporal variation - Do we need to?

Midpoints vs Endpoints
16

Summary

ISO Standards: Life Cycle Assessment &

Comparative Assertions

ISO 14040 defines a comparative assertion as an "environmental claim regarding the superiority or equivalence of

one product versus a competing product which performs

the same function." The ISO 14020 series establishes several principles for any environmental claim, including comparative assertions: information will be accurate, verifiable,

relevant, and non-deceptive; scientific methods will used to

generate the results; the process is open and participatory;

the information is transparent and available to all (e.g., purchasers, interested parties, etc.); any claim is based on

measurable differences including consideration of variations

and uncertainty; and clear explanatory statements justify

and qualify the claim. The 14040 LCA standards and draft

standards meet these principles.

ISO 14040 and 14041 provide a detailed framework covering the LCA study goal definition and scope; functional

unit and system boundaries; data inclusion and exclusion;

data quality; data collection, handling, and calculations; allocations; and reporting. There are also specific requirements

when the comparative assertion is disclosed to the public:

data quality parameters that consider time-related, geographical, and technological variations; data precision, completeness and representativeness for different processes;

consistency and reproducibility of the methods; data

sources; and uncertainty. Public comparative assertions also

require equivalence between compared systems for functional unit, methodological considerations, performance, system boundaries, data quality, allocation procedures, decision rules on inputs and outputs, and the impact assessment phase. Together, these requirements establish a fair

comparison in the inventory phase that is technically sound,

transparent, and non-deceptive.

ISO 14040 requires that public comparative assertions

be based on a full LCA, including an impact assessment

phase. The mandatory portions of impact assessment in

FDIS 14042 are Figure 1. The concept of an LCIA category

indicator and its relationship to the impact category and the

characterization model is shown in Figure 2.

ISO 14042 requires categories, indicators, and models

used be: consistent with study goal and scope, sources for

models, etc., be referenced; selections be transparently justified and explained; a comprehensive set of environmental

issues within the goal and scope be covered; and describe

environmental mechanism and model so that the LCI results are related to the indicator, and the appropriateness of

model is understandable. A scientific approach in the impact category model is taken by basing the model on a

defined environmental mechanism, i.e., a system of physical, chemical, and biological processes linking category LCI

results to category indicators and to category endpoints. A

highly simplified example for an acidification environmental

mechanism is shown in Figure 3.

ISO 14042 requires for public comparative assertions: a

sufficiently comprehensive set of indicators, a comparison

conducted indicator by indicator, that LCIAs not be sole

basis for comparative assertions as additional information

may be necessary to overcome limitations in clause 8 of

14042), that subjective scores such as weighting shall not

be used; that category indicators used shall be both 1) scientifically and technically valid, and 2) environmentally relevant, and sensitivity and uncertainty analyses of results

shall be conducted.

Thus, each of the mandatory steps used to derive an

indicator must be scientifically and technically valid: grouping into impact categories (Classification), converting LCI

results (1st Characterization step), and aggregating converted LCI results (2nd step). This may present difficulties

for several current practices such as aggregation of different effects, aggregation of a similar effect from different

places and times, and the use of subjective scores. For

example, an ILSI expert panel to toxicologists states that

“It is inherently impossible to make a purely scientific comparison of qualitatively or quantitatively different toxicity

impacts. This type of aggregation is equivalent to comparing the impacts of global warming and acid rain. Such comparisons cannot be done scientifically.”

For environmental relevance, ISO 14042 establishes key

criteria to meet: the indicator will reflect the actual conseISO Standards: Life Cycle Assessment & Comparative Assertions

J. Willie Owens,

Procter & Gamble

Cincinnati, OH, USA
17

quences of the system operation on the category

endpoint(s), at least qualitatively. In addition, the category

model must incorporate environmental data or information,

including: environmental condition of the category

endpoint(s); intensity of environmental changes; spatial and

temporal aspects such as duration, residence time, persistence, timing, etc.; reversibility; and uncertainty.

ISO also established a critical review of any study used

for public comparative assertions so that methods used will

be consistent with ISO, all methods are scientifically and

technically valid, the data are appropriate and reasonable,

study interpretations reflect the limitations, and that the study

report is transparent and consistent. The panel members

should be familiar with ISO and have scientific/technical

expertise to address the impact categories covered. There

are also extensive reporting requirements for the conduct

of a study to ensure transparency, the critical review panel

report must included in study report, and the report must be

made available to all upon request.

As a result, the ISO 14040 series clearly complies with

the 14020 series principles for environmental claims made

in the opening paragraph.

Thought Piece: Comments on Science

First, science is the exploration of the physical world and

the body of knowledge that results from that exploration.

This knowledge is gained via a number of processes including basic research, applied research, and applications

such as environmental monitoring.

Second, this body of knowledge is both gained and assessed by applying the scientific method. The tenets or

characteristics of the scientific method are:

• The method begins with defined and framed observations or questions.

• A testable hypothesis is generated to explain the specific observation or address the specific question. The

hypothesis is then used to design physical experiments.

This includes clear descriptions of assumptions to be

used and variables to be tested.

• The experiments include positive and negative controls.

In many cases, statistical tests of the results and the

variables are also included.

• The experimental observations are based on results that

are physically measurable and obtained using validated

measurement techniques.

• The hypothesis, methods and results are transparently

described so that

1. Both the hypothesis and the experiment can be understood, reproduced and the results verified.

2. The hypothesis can be tested in other experiments

with different designs and measures.

• An interpretation consistent with the experiment is offered, including care to distinguish the statistical support, to note alternative interpretations where they exist, and to carefully qualify additional hypotheses or

speculations that are not directly supported by the results.

• Independent researchers can then explore the hypothesis and findings where new experiments may support

or challenge the results and interpretation. The ultimate

outcome is acceptance or rejection of the hypothesis.

Third, the last point has major implications. Science rarely

offers absolute proof, and cannot prove a negative or null

hypothesis. The body of knowledge generated by the scientific method is subject to assessment with new experimental evidence, and even periods of controversy when matters are disputed. In fact, certain findings can be highly

contentious when established models and thinking are challenged by new experiments. The acceptance of these new

experiments then awaits testing, verification, and understanding. For example, a strong debate continues among

evolutionary scientists between continuous evolutionary

change and another model termed punctuated equilibrium.

In another case, proposals for a synergistic effect among

so-called endocrine disrupters could not be reproduced either in other laboratories or the original laboratory, so the

authors retracted their findings. Thus, science and its body

of knowledge is not dogma. Science operates by constructing and assessing the weight of evidence.

Fourth, scientific weight of the evidence is often a gradual

accumulation of data and knowledge. For example, the

empirical observation of PCB body residues in Baltic fauna

in the mid-1960s led to the reproduction of this observation

in other areas and species as well as with other compounds

such as DDT. As a result, we now have detailed body of

knowledge about:

- the physical and biological properties underlying the fate

and transport of substances,

- the degradation and persistence of substances,

- their many mechanisms of toxicity,

- the processes of bioaccumulation and biomagnification,

and

- the means to measure and model these processes as

well as how to validate the models.

Owens’ Biography

Owens is a Principal Scientist at Procter & Gamble and

has responsibility for LCA development. He has a B.S. in

Biology (summa cum laude, Christian Brothers College,

Memphis, TN) and Ph.D. in Molecular Biology (US National

Science Foundation Fellow, Washington University, St. Louis,

MO). His prior experience contributing to his LCA background

includes product development, manufacturing, and consumer

testing within P&G, such as upstream product development
18

for both disposable medical products and industrial cellulose products, pilot plant experience in modified cellulose

fiber production, in-plant process development experience

at pulp mills, pilot scale production and consumer testing of

consumer paper products, service as a human toxicologist

for pulp and paper products, and environmental assessment

studies at pulp and paper mills. The largest environmental

study involved the design and the execution of the

multidisciplinary and multi-year effort in Alberta, Canada.

He has participated in the conduct of several life-cycle studies within P&G. He is a member of SETAC’s LCA Advisory

Board, chair of the SETAC LCA Work Group on Impact

Assessment (North America), and is a member of the U.S.

delegation chair for the ISO 14000 TC 207 LCIA Work Group.
19

ISO Standards: ISO Standards:

Life Cycle Assessment Life Cycle Assessment

& Comparative Assertions & Comparative Assertions

J. William Owens

Procter & Gamble

Cincinnati, OH, USA

UNEP LCIA Workshop

Nov. 29-30, 1998

Brussels

Definition of Definition of

Comparative Assertions Comparative Assertions

14040 - Definition 3.2

environmental claim

 regarding the superiority or equivalence

 of one product versus a competing product

 which performs the same function

ISO 14000 Standards

Normative Terminology

shall - requirement;

 critical review would note whether or not

 study was in compliance

should - recommendation;

 critical review would note whether or not

 recommendation was followed

may - suggests consideration

ISO 14020 Series

Declarations & Labels

Necessary elements for claims

• Accurate, verifiable, relevant, and non-deceptive

• Scientific method used where results are

 are accurate and reproducible

• Open, participatory process

• Information available to purchasers, interested

 parties, etc. (transparent)

ISO 14020 Series ISO 14020 Series

Declarations & Claims

Other elements:

• Based on measurable difference;

 analysing data ranges and variability

• Explanatory statements where needed

• Relevant to the geographic area

 Life cycle assessment standards are

 consistent with these requirements

ISO 14040 and 14041

Coverage

• Goal definition and scope

• Functional unit and system boundaries

• Data inclusion and exclusion for inventory

• Data quality

• Data collection, handling, and calculations

• Energy data handling and reporting

• Allocations

• Reporting

 Accurate, technically sound, verifiable, & transparent

Brief Overview of Areas

Owen’s Overheads
20

Goal Definition & Scope

- the functions of system or systems

- the functional unit ;

- the product system to be studied

- the product system boundaries;

- allocation procedures;

- categories, methodology, and interpretation of impact

 assessment (also see Appendix A of 14042);

- data requirements;

- assumptions;

- limitations;

- initial data quality requirements

- type of critical review, if any ;

- type and format of study report.

Clearly define application & audience as well as:

Data Quality

All the data quality parameters must be addressed

and included in the study

- time-related

- geographical 

- technology

- precision, completeness and representativeness

 of data

- consistency and reproducibility of the methods

- data sources and their representativeness

- uncertainty

Comparative assertions

Equivalence of Comparisons

Equivalence necessary to make valid comparisons:

- same functional unit;

- equivalent methodological considerations:

 performance,

 system boundaries,

 data quality,

 allocation procedures,

 decision rules on inputs and outputs and

 impact assessment.

 Non-deceptive & based on measurable difference

Comparative Assertions - 14040

Life Cycle Impact Assessment Life Cycle Impact Assessment

4 phases of LCA LCI LCA

- goal and scope definition + + 

- inventory analysis + +

- impact assessment +

- interpretation + +

LCIA phase necessary for comparative assertions

Discussion for Discussion for

Mandatory Elements Only

Category indicator results (LCIA profile)

Selection of impact categories,

category indictors and models

Assignment of LCI results [Classification]

Calculation of category indicator results

[Characterisation]

Life cycle inventory results

Impact

Category LCI results assigned to

impact category

Category Indicator

Category Endpoint(s)

Model

Environmental relevance

Acidification

Acidifying Emissions

(NOx, SOx, etc.)

Proton Release (H+)

- forest

- vegetation

- etc.

Example

kg SO2, HCl, etc.

Category Indicator Category Indicator
21

Goal Definition & Scope Goal Definition & Scope

- the functions of system or systems

- the functional unit ;

- the product system to be studied

- the product system boundaries;

- allocation procedures;

- categories, methodology, and interpretation of impact

 assessment (also see Appendix A of 14042);

- data requirements;

- assumptions;

- limitations;

- initial data quality requirements

- type of critical review, if any ;

- type and format of study report.

Clearly define application & audience as well as:

Data Quality

All the data quality parameters must be addressed

and included in the study

- time-related

- geographical 

- technology

- precision, completeness and representativeness

 of data

- consistency and reproducibility of the methods

- data sources and their representativeness

- uncertainty

Comparative assertions

Equivalence of Comparisons Equivalence of Comparisons

Equivalence necessary to make valid comparisons:

- same functional unit;

- equivalent methodological considerations:

 performance,

 system boundaries,

 data quality,

 allocation procedures,

 decision rules on inputs and outputs and

 impact assessment.

 Non-deceptive & based on measurable difference

Comparative Assertions - 14040

Life Cycle Impact Assessment Life Cycle Impact Assessment

4 phases of LCA LCI LCA

- goal and scope definition + + 

- inventory analysis + +

- impact assessment +

- interpretation + +

LCIA phase necessary for comparative assertions

Discussion for Discussion for

Mandatory Elements Only

Category indicator results (LCIA profile)

Selection of impact categories,

category indictors and models

Assignment of LCI results [Classification]

Calculation of category indicator results

[Characterisation]

Life cycle inventory results

Impact

Category LCI results assigned to

impact category

Category Indicator

Category Endpoint(s)

Model

Environmental relevance

Acidification

Acidifying Emissions

(NOx, SOx, etc.)

Proton Release (H+)

- forest

- vegetation

- etc.

Example

kg SO2, HCl, etc.

Category Indicator Category Indicator
22

Algebraic Formula of Calculations

The conversion of the inventory result is

a multiplication step - in its simplest form:

 CIR = CF * IR

where

 CIR - converted inventory result

 CF - characterisation factor

 IR - inventory result

or algebraically:

 y = a * x

For LCI Result Conversion For LCI Result Conversion

When seen as CIR = CF * IR:

y = a * x

Converted

Inventory

Result

Inventory Result

(IR) is x-axis

slope (a) is Characterisation Factor

Linear response

Zero intercept

No threshold

AGGREGATION

Is Aggregation of Converted LCI results Valid?

Do the LCI results truly act and combine together?

Are the LCI results interchangeable across

spatial and temporal scales?

or

Are the LCI results independent in the environment,

where action in one place or

time has no connection to another?

Σ?

HUMAN TOXICITY

 Issue Toxicologists LCA

Thresholds Exist Absent

Dose-Response Case-Specific Linear

Endpoint Additivity Common Mode / Universal

 Common Organ

Exposure* Case & Circumstance Simultaneous

 Specific & Aggregated

*Assumes common place and time in aggregation

Current Issues

HUMAN TOXICITY HUMAN TOXICITY

ILSI Panel on Aggregation

Burke, Doull, McKone, Paustenbach, Scheuplein, 

Udo de Haes, and Young

“It is inherently impossible to make a purely

scientific comparison of qualitatively or

quantitatively different toxicity impacts. 

This type of aggregation is equivalent to

comparing the impacts of global warming

and acid rain. Such a comparisons cannot 

be done scientifically.”

Indicator reflects the consequences on the category

 endpoint(s), at least qualitatively.

Incorporate environmental data or information in the

 category model, including:

- environmental condition of the category endpoint(s)

- intensity of the projected impacts,

- spatial extent of projected impacts ,

- temporal aspects, duration, residence time, 

 persistence, timing, etc., of projected impacts,

- reversibility of projected impacts, and

- uncertainty of projected impacts

Criteria for Environmental Relevance Criteria for Environmental Relevance
23

Critical Review

Sum m ary of requirem ents

- m ethods consistent w ith ISO

- m ethods are scientifically and technically valid

- data are appropriate and reasonable

- interpretations reflect the lim itations

- report is transparent and consistent

Panel requirem ents - 14040 & 14042

- fam iliar with ISO and scientific/technical expertise

- m ay include other parties effected by study

- panel report included in study report

- expertise for im pact categories covered

Conclusions

• Procedures technical & scientific valid so that

 analysis is credible to all parties

• R esults w ill be environm entally relevant

• Procedures, assum ptions, and decisions transparent

• Independent review of the study and its conclusions

• Flexibility for different applications and for

 current and future m ethods developm ent

• O pen and equal treatm ent to industry, NG Os, 

 governm ent, consultants, universities, etc.

The 14040 LCA standards
24

Introduction

The problem with LCA and ecodesign can be summarized as:

• LCAs are too time consuming in product development

processes.

• The results of LCAs are hard to interpret for a designer;

often ten different scores for different environmental

impact categories are presented as “the” answer.

Our solution: Develop a set of predefined scores for the

most commonly used materials and processes. The set

should include indicators for common materials, production

processes, transport, energy consumption and different disposal options per material. With this set anyone can perform its own LCA in a matter of minutes on the back of an

envelope.

A crucial factor in this solution is the reliability of the

indicators. The first effort to develop this set of indicators

resulted in the Eco-indicator 95 LCIA methodology, which

now in use by many companies. Currently a follow-up project

is undertaken for the Dutch government resulting in the Ecoindicator 99 methodology (Goedkoop 98).

Calculating standard indicators

Standard indicators are calculated with the LCA methodology. This means we have to deal with the three different

aspects of any LCA: inventory, impact assessment and

valuation issues. In more abstract terms we have described

these three different issues as ”spheres”:

• Technosphere, describing the technical system, or the

inventory phase.

• Ecosphere, describing the environmental cause and effect mechanisms, resulting in a number of damage categories or endpoints.

• Valuesphere, describing the societal values in assessing the seriousness of the damages, and describing

some of the value choices that are unavoidable in the

modeling of cause and effect mechanisms.

Each sphere has its own typical characteristics and problems.

Technosphere Ecosphere Valuesphere

Subject Concrete technical Models complex Societal preferof the systems cause and effect ences and

modeling: chains values

Verification Possible in many Difficult or A single truth

cases impossible does not exist

Main Boundaries Limited under- How do we

problems: Allocations standing of measure

values mechanisms in society?

and data How do we

problems deal with

incompatible

views

Uncertainty low (far less then High (sometimes High, depending

an order of magni- several orders of on procedure

tude) magnitude for information

gathering

The problems in technosphere are relatively well understood

in LCA. The problems in ecosphere can in principle be solved

as our scientific understanding progresses, although considerable research efforts will still be needed. The problems in

value sphere are fundamental, as they deal with incompatible

views of different people. These problems cannot really be

solved, but they can be managed if they are well understood.

The nature of the valuesphere problems can be illustrated

with two examples.

Example 1: What level of scientific proof is required?

For only a few substances the carcinogenicity is sufficiently

proven. Many substances are generally suspected to be carcinogenic, but scientific proof is incomplete. Some people will

maintain that all substances under suspicion must be included,

while others would demand sufficient scientific proof if a substance were to be included. Who will decide?

Example 2: What timeframe do we adopt?

Impact Assessment for Ecodesign

Mark Goedkoop, PRé Consultants, The Netherlands,

Email: Goedkoop@pre.nl
25

In fate models an important parameter is the time perspective. If we take a long time perspective, substances

like heavy metals become very dominant in the analysis. In

fact the choice of the time perspective determines the characterization values to a great extent. Some people will argue an indefinite time perspective must be used, while others would argue it is nonsense to look at damages that

would occur after 100 years. Who will choose the time perspective, and thus decide on the relative importance of heavy

metals?

In a system with standard indicators such questions become very important, as, unlike in a full LCA, the stakeholders can not make such decisions on a case by case

basis. In the Eco-indicator 99 methodology the solution is

to choose three different perspectives. This means we produce three different versions of this methodology, each with

its own consistent set of value choices. (See also [Hofstetter

1998] and [Thompson 1990] on Cultural Theory).

• Egalitarian: Precautionary principle: include as much

as possible, use a long time perspective. (Typical attitude of environmental groups)

• Hierarchical: Include facts that are backed up by scientific and political bodies. (Typical attitude of scientists).

• Individualist: Only proven cause effect relations, short

time perspective; future damages are far less important then present damages. (typical attitude of entrepreneurs)

The users of the indicator system cannot make value

choices on a case by case basis, but they can choose

between three sets of value choices. This underlines the

obvious fact that a single truth is not available in LCA, even

when indicators are used.

Conclusions

• It is much more efficient to calculate a large set of reliable single scores for commonly used materials and

processes. With these any designer can make their own

LCA without the help of other experts.

• The issue of value choices becomes very important in

a system with predefined indicators, as the user cannot influence them, while the results they get partially

depends on such choices.

• By separating three different perspectives we give the

user the opportunity to choose a perspective he can

adhere to, and we show that a single truth does not

exist.

References

Goedkoop, M.J., Hofstetter, P., Müller-Wenk, R., Spriensma,

R. The Eco-indicator 98 Explained, International Journal on LCA, December 98, ECOMED Publishers,

München

Hofstetter, P. (1998): Perspectives in Life Cycle Impact

Assessment; A Structured Approach to Combine Models of the Technosphere, Ecosphere and Valuesphere. ,

Kluwers Academic Publishers, 1998, Info: www.wkap.nl/

book.htm/07923-8377-X.

Thompson M,, Ellis R., Wildavsky A.; Cultural Theory,

Westview Print Boulder 1990

Goedkoop's Biography

Goedkoop is a Consultant and Scientist, born in 1954.

After obtaining an Electronic Engineering degree at Alkmaar

Polytechnic and an Industrial Design Engineering degree at

Delft University of Technology in 1983, he established an

industrial design engineering consultancy, called Evegoed.

In 1990 he shifted the attention to Ecodesign and Life Cycle

Assessment and renamed the consultancy as PRé

Consultancy. (see www.pre.nl)

• PRé was in 1990 the first Dutch design consultancy

exclusively devoted to Eco-design and LCA. We executed one of the first LCA in the Netherlands.

• From the start we worked on the development of LCAbased tools for Eco-design, like SimaPro LCA software

(introduced in 1990) and the development of Eco-indicator 95 methodology. SimaPro is by far the most widely

used LCA software world-wide.

• Development of ECO-it, a low cost, simple tool for designers, based on Eco-indicators.

The most important projects run by Goedkoop are

• Active participation in seven Eco-design projects under the two National Dutch Ecodesign projects, as well

as participation in several other Ecodesign-type projects.

• Project leader for the Eco-indicator 95 Methodology under commission from The Dutch NOH project.

Project leader for the follow up project, the Eco-indicator

97/98 under commission of the Dutch ministry of Environment, in collaboration with several companies and well known

LCA experts in the Netherlands and Switzerland.
26

Goedkoop's Overheads

Impact assessment for Ecodesign

Mark Goedkoop

PRé Consultants B.V. Plotterweg 12 3821BB Amersfoort The Netherlands

E-mail: goedkoop@pre.nl web site: www.pre.nl

The problem with LCA and ecodesign

• LCA ́s are too time consuming in product

development processes.

• The results of LCA ́s are hard to interpret for a

designer; often ten different scores for different

environmental impact categories are presented

as “the” answer.

• Designers will neve become LCA experts.

They may not stay dependent on experts

Stages in the design process

Stage LCA tool

Product idea generation Trends, experience,

policy, etc

Definition of

requirements

LCA of reference, calc.

of new indicators

Idea generation Eco-indicators

Concept development Eco-indicators

Detailed design LCA for verification

Example: LCA of a coffee machine

assembly

poly- aluminium

extrusion

+ transport

disposal in

municipal

waste

disposal of

in org. waste

use

paper

 duction

 filter prosheet steel

stamping

forming

glas

forming

filters + coffee

coffee 

roasting

packaging

water

injection

moulding

bean styrene

electricity

Result of coffee machine analysis

 polystyrene

injection

aluminium

extrusion

 assembly

 municipal waste municipal waste

electricity

paper glass glass

forming

7.3 kg 1 kg 0.1 kg 0.3 kg 0.4 kg

375 kWh use

moulding

steel steel

Our solution: Pre-defined single

scores

• Simplification of the methodology is not the

solution.

– The results will not be reliable

– Speed improvements are not sufficient

• Pre-defined Single scores are the solution...

• Important requirement: LCAs based on

single scores should predict results of later

full LCAs
27

Calculating single scores

• In order to make predefined scores extremely

careful modelling is needed.

• The model should contain all three spheres *):

– Techno-sphere,

– Eco-sphere and

– Value-sphere

• Different value systems can now be made

explicit. ) See also: Hofstetter, P. (1998): Perspectives in Life Cycle Impact Assessment; A

Structured Approach to Combine Models of the Technosphere, Ecosphere and

Valuesphere. , Kluwers Academic Publishers, 1998

The three Spheres

Inventory 

Table:

Resources

Land-use

Emissions

Damage to

Resources

Damage to 

eco-system 

health

Damage to 

human 

health

Subjective 

valuation 

of the 

seriousness 

of damages 

mainly in:

Valuesphere

Modelling

of cause

and

effect chain

mainly in:

Ecosphere

Indicator

Modelling

of the 

lifecycle

mainly in:

Technosphere

Modelling Technosphere

Inventory

table

Raw 

materials

Land use

CO2

VOS 

P 

SO2

NOx

CFC

Cd

PAH

DDT

Inventory Phase

product

electr.

steel

plastic

distr. use

reuse

recycl.

inciner.

dump

Typical characteristics Techno-sphere

• Description of concrete technical systems

• Uncertainties can be low (far less then an

order of magnitude)

• Verification is possible in many cases

• Main problems:

– Boundaries

– Allocations

Modelling Eco-sphere

Raw 

materials

Land use

Emissions

Lower

concentrations

Damage to

Resources

Damage to 

eco-system 

health

Damage to 

human 

health 

Concentrations in air, 

water, soil 

and food

Effect of 

land-use

Impact 

categories 

Resource 

analysis

Fate 

analysis

Characterisation

Damage

analysis

Typical characteristics Eco-sphere

• Models complex cause and effect chains

• Usually high uncertainties (sometimes

several orders of magnitude)

• Verification is difficult or impossible

• Main problems:

– Limited scientific understanding of mechanisms

– Limited availability of data

– Different perspectives are possible
28

Modelling Value-sphere

Damage to

Resources

Damage to 

eco-system 

health

Damage to 

human 

health 

Relative

damage to

Resources

Relative

damage to 

eco-systems 

Relative

damage to 

human health

Normalisation

Eco-indicator

value

Damage 

Assessment

Typical characteristics of Value-sphere

• Models aspects natural science cannot take

into account: “seriousness”

• Uses social science

• A single truth does not exist

• Main problems:

– How do we measure values in society

– How do we deal with incompatible views

The three Spheres

Inventory 

Table:

Resources

Land-use

Emissions

Damage to

Resources

Damage to 

eco-system 

health

Damage to 

human 

health

Subjective 

valuation 

of the 

seriousness 

of damages 

mainly in:

Valuesphere

Modelling

of cause

and

effect chain

mainly in:

Ecosphere

Indicator

Modelling

of the 

lifecycle

mainly in:

Technosphere

Values in Eco-sphere

Example: There are three classes of carcinogenic

substances:

– Carcinogenic effect is proven

– Carcinogenic effect is probable

– Carcinogenic effect is possible

Do we include all classes?

Who will decide this?

Cultural theory as management

system of subjectivity

Three types of perspectives:

– Egalitarian: Future generations just as

important as present. In case of uncertainty take

the worst case.

– Individualist: Limit time scale, In case of

uncertainty take proven facts only.

– Hierarchist: Base analysis on what is agreed

upon in policies and science.

Three different ecosystem models are needed!

Three instead of one single score

• We will develop three alternative sets of

single scores.

• Each set represents a cultural perspective.

• If A is better then B in all perspectives, then

the result is robust.

• If A is not better then B, the result is

dependent on perspective
29

Compatibility with other

approaches

• Based on damage approach

• Themes are made explicit as intermediate results

• Characterisation not always based on

equivalency, but damages

• If ISO compatibility is required, final weighting

may not be used in comparative assertions.

• Eco-indicator society should promote proper

application

Overview Eco-indicator 98

Raw 

materials

Land use

Emissions

Lower

concentra -

tions

Damage to

Resources

Damage to 

eco-system 

health

Damage to 

human 

health 

Concentrations in air, 

water, soil 

and food

Effect of 

land-use

Greenh

Ozone dep.

Acid/nutri

Ecotox.

Resource Hum tox

analysis

Fate 

analysis

Characterisation

Damage

analysis

Relative

damage to

Resources

Relative

damage to 

eco-systems 

Relative

damage to 

human health

Normalisation

Eco-indicator

value

Damage 

Assessment

Resources 

Conclusions

• Single scores need very careful modelling, as

they need to predict the outcomes of full

LCAs

• By separating three different perspectives we

acknowledge the fact that different views are

possible, and that a single truth does not exist

(unlike most full LCAs)

• Modelling three spheres is essential

• This method can also be used in full LCA
30

The reason to perform an LCA is essentially to use it in

support of a decision. A decision gives rise to a change

somewhere in society compared to a scenario in which this

decision was not taken. The key requirement for the LCA in

any application is, therefore, that it shall reflect the environmental change caused by the decision. It is found that the

need to differentiate LCA methodology for the use in different applications is born by a few key characteristics of the

decision to be supported.

The first key characteristic is the environmental consequence of the decision, i.e. the nature and extent of the

environmental change caused by the decision. When modeling the environmental change, its extent in time and space

will differ between decision types, thus giving rise to different requirements, primarily for the scoping and inventory

phases of the LCA. Furthermore, some decisions will imply

trade-offs between different impact categories, while others

will not, thus causing different requirements for the impact

assessment. The second key characteristic is the social

and economic consequence of the decision, the magnitude

of which will influence the need for certainty, transparency

and documentation. The third characteristic is the context

in which the decision is taken, including the decision-maker

and interested parties, implicitly influencing the impact assessment and weighting.

Wenzel’s Biography

Wenzel has expertise in environmental Life Cycle Assessment of products (LCA) and environmental design of products. Research and development of methods and tools.

Special experience with electronics and electro-mechanical products, textiles, packaging, furniture and energy systems.

Wenzel has experience in Cleaner Production: Environmental auditing, identification of options, research and technology development, implementation and dissemination.

Special experience within water based industry including

textile industry, paper industry, industrial laundries. Expertise in reclamation and reuse of water and waterborne energy and chemicals. Development of methods and procedures.

He also has expertise in environmental impact assessment of industrial effluents, and industrial wastewater treatment and control. Special experience with chemical industry, pharmaceutical industry and pulp industry. Development

of methods.

Application Dependency of LCIA

Henrik Wenzel, Tech U of Denmark
31

Wenzil’s Overheads

Application Dependency of LCIA

Henrik Wenzel

Institute for Product Development

Technical University of Denmark, Building 424

DK 2800 Lyngby

Denmark

e-mail: wenzel@ipt.dtu.dk

LCA Applications

- some of the most useful

Companies Authorities

Life Cycle Management X

Strategic Planning X X

Product Development X

Purchase X X

Process Design/Selection X X

Marketing

- active claims

- passive information

X

X

X

X

Goal Definition

The proof of the pudding lies in the eating

In plain language: you know it’s good if it’s used

More philosophically:

the identity of a thing is borne by its being used for its purpose

Goal and Scope Definition

The decision

maker

Scope Definition

No decision / no env. consequence

- who cares about what method was used?

Decision / env. consequences?

- defined scope and inventory data should

reflect the caused environmental changes

= ΣA B ∆ CO2

Application dependency of LCA

- hypothesis

Three main variables govern application dependency:

1. The environmental consequence of the decision

- time scale

- spatial scale

2. The economic/social consequence of the decision

3. The decision context

- the decision maker

- interested parties

- implicit assessment criteria
32

The Environmental Consequence of the Decision

- differentiation of LCA applications

Figure 4. Examples of LCA applications which differ in the time scale of the

decision and in their need for site specific information

Need for site specific of information

Societal action plans

and legislation

Environmental labeling

- criteria setting

Product development

Production technology assessment

Supplier requirements

Marketing

Time scale of decision (years)

20

15

10

5

0

0

The Economic/Social Consequence of the Decision

- differentiation of LCA application

Figure 5. Examples of LCA applications with different need for certainty,

transparency and documentation caused by difference in economic or social

consequence of the decision to be supported

Need for certainty, transparency and documentation

Product development

- products with small

 environmental focus

Eco-labeling

- criteria setting

Societal action plans

and legislation

Economic or social consequence

LCA Applications in 3 Governing Dimensions

Need for certainty, transparency

 & documentation

Eco-labeling

criteria

Societal

action plans

Product

development Marketing

- active

 claims

Purchase

Time scale

- passive

Site speciality

Simplification of LCA Methods

Consensus: simplification is about delimitation and focusing. An

iterative procedure using all phases of LCA in increasing level of detail.

Matrix LCA

- qualitative or

semiquantative

Screening LCA

- quantitative

- readily available data

Full LCA

- quantitative

- new data

inventory

Figure 3. Three iterations of LCA

Extent of Work Required at Different LCA Levels

- rough average time estimates

Extent of Work (days)

Matrix LCA Screening LCA Full LCA

100

10

1

LCA Simplification and Iterative Procedure

- the reasons to continue with next iteration

Semi-quantitative

Matrix LCA

Quantitative LCA

- readily

available data

Quantitative

LCA

- new data

inventory

Is there a trade-off?

Can you conclude without impact

assessment?

Are data readily available?

Can you conclude without new data

inventory?
33

LCA Simplification and Iterative Procedure

- continue as far as needed

The three iterations of the LCA supports different decisions and conclusions

The LCA application = each single conclusion/decision

Semi-quantitative

Matrix LCA

Quantitative LCA

- readily

available data

Quantitative

LCA

- new data

inventory

Resource consumptions LC Source - Pro- Sub- Improveme

and environmental impacts stages types cesses stances potentials

1. xxxxx 2. xxx 3. xxx 5. xxx 6. xxx 7. xxx

xxxxxxx xxxxx xxxxx xxxxx xxxxx xxxxx

4. xxx 8. xxx

xxxxx xxxxx

9. xxxxx 10. xx 11. xx 13. xx 15. xx

xxxxxxx xxxxx xxxxx xxxxx xxxxx

12. xx 14. xx 16. xx

xxxxx xxxxx xxxxx

17. xxxx 18. xx 19. xx 20. xx

xxxxxxx xxxxx xxxxx xxxxx

Conclusions: the most essential....

The Decision Context

1. Implicit assessment criteria

2. Implicit weighting criteria

- e.g. national priorities in LCA for national strategies

 and action plans

3. Interpretable interpretation support

- e.g. Dutch normalisation and weighting for a Dutch

 decision maker?

Application Dependency of LCIA

1. LCIA only needed when LCA is applied on decisions

involving a trade-off

2. There is a need for sophistication of site dependency

in LCIA when decisions involving trade-offs cannot be

supported without it

3. The need for site dependency in LCIA is largest for

applications of LCA to trade-off decisions for which the

consequences are limited to few specific sites

4. The need for sophistication of LCIA is largest in trade-off

decisions with highest requirements for certainty

5. The decision context may imply certain weighting criteria

6. The background and knowledge of the decision maker

may influence the choice of normalisation/weighting
34

A framework for uncertainty analysis, which was originally developed to risk assessment (Finkel 1990), is applied to the exposure modeling component of the Human

Toxicity Potential (HTP) (Hertwich 1999). The HTP is a

weighting factor, similar to that goal warming potential, that

is used to multiply emissions in a life cycle inventory to

obtain a single metric representing the human health hazard (Hertwich et al. 1998). The HTP presents evaluations of

hazard based on the toxic potency of a substance and the

potential dose in a so-called unit world. The toxic potency is

expressed by cancer potency factors q1* or the inverse of

the allowable daily intake or reference dose. The exposure

is calculated using CalTOX (McKone 1993; Maddalena et

al. 1995), a risk assessment model that integrates a multimedia environmental fate model with a multiple pathway

exposure model. We distinguish between carcinogens and

non-cancer effects that are expressed in benzene and toluene equivalents, respectively. For the same chemical, the

HTP will depend on the compartment of the release. HTP

comes significantly closer to an actual risk assessment

than alternative approaches without requiring site-specific

input data (Hertwich et al. 1998). HTP values were calculated for approximately 300 chemicals representing > 80%

of the air emissions reported by U.S. manufacturing facilities in the TRI. HTP values for air and surface water emissions can be downloaded from http://www.scorecard.org/

env-releases/def/tep_gen.html

The framework for uncertainty analysis distinguishes

among the uncertainty and variability in input parameters,

model uncertainty, and the decision rule uncertainty.

1. Parameter uncertainty: measurement errors (resolution of instrumentation), random errors (sampling errors-representativeness of entire population) and systematic errors (biases introduced through experimental

design or instruments; e.g., the “healthy worker effect”

according to which epidemiological studies are not representative for the general population because workers

in general are of better health than the average citizen.)

Parameter uncertainty captures the possible errors associated with the inputs to mathematical models. These

uncertainties can also be due to estimation methods

and guesses.

The CalTOX model uses three different sets of parameters: chemical-specific parameters (decay rates, volatility), landscape parameters (biomass inventory, precipitation rate), and exposure parameters (diets, breathing rates). For the purpose of the present assessment,

we assume that chemical-specific data is uncertain,

whereas landscape and exposure parameters are variable.

The uncertainty associated with chemical specific input parameters was investigated using Monte Carlo

analysis (Hertwich et al. in press). First, chemicals were

grouped according to the most important exposure route.

For each group, a representative chemical was selected.

For each of these chemicals, Monte Carlo simulations

were conducted and the importance of the input parameters was evaluated using rank correlation. The analysis indicates that the uncertainty varies between 1/2

and 3 orders of magnitude, but is commonly around

one order of magnitude. This is little compared to a variation of potential dose among chemicals of 7 orders of

magnitude and indicates that there is a significant gain

in information from modeling the potential dose. The

most important input parameters were reaction half lives,

as a sensitivity analysis indicates. The results show

that the uncertainty due to the exposure modeling can

be as large as the uncertainty in the toxic potencies,

which has been evaluated by others (Gaylor et al. 1993;

Baird et al. 1996).

2. Variability expresses the known natural variation of specific parameters, such as diets, proximity to emission

sources, temperatures and weather patterns, annual or

diurnal variation of emissions rates, transformation rates,

and transport. Variability differs from parameter uncertainty because it is not related to how precisely we can

measure a specific quantity but it is due to natural variations of a parameter.

The influence of variability in landscape and exposure

parameters was investigated together with the parameter uncertainty using Monte Carlo analysis, as described above (Hertwich et al. 1998). For most chemiTypes of Uncertainty in the Human Toxicity Potential

Edgar Hertwich

University of California

 Berkeley, CA, USA
35

cals, the variation in landscape and exposure parameters is insignificant compared to the uncertainty in

chemical specific parameters. In some cases differences in exposure parameters have a significant effect. What about uncertainty in the exposure parameters?

3. True model uncertainty captures the uncertainty that

is introduced through making models, the idealization

of reality. Model uncertainty includes the uncertainties

introduced by surrogates (the uses of rodents as a model

for toxic effects in humans); through the exclusion of

specific mechanisms; through the inability of models

to capture abnormal conditions which may have a large

impact on the outcome; and through incorrect model

form (errors in the model formulation, or insufficient

knowledge about the true nature of the investigated

model processes). An example for model uncertainty is

that the shape of the dose-response curve in cancer

risk assessment is usually not known, and the selection of any specific model has a large impact on the

predicted cancer risk at ambient environmental exposures (low doses) from the extrapolation of rodent test

results at high doses (Chiu et al. 1999).

Two examples of model uncertainty were investigated:

1) Two alternative model formulations for the air-plant

interactions in the multimedia model CalTOX were compared. For half of the chemicals, the concentration in

the plant compartment differed significantly. Significant

differences in human exposure were observed only for

20% of the chemicals, and the differences were always

below a factor of 5.

2) The effect of the steady state assumption for wet

deposition was investigated. For 10% of the chemicals,

the continuous rain led to a significantly lower air concentration than a situation without rain. This led to differences in exposure of up to 2 orders of magnitude.

The steady state assumption for wet deposition needs

to be questioned for chemicals with low Henry’s Law

constants. While the rainfall rate has little difference on

the steady-state results, the total absence of rain does.

This problem was addressed by combining the model

results with and without rain.

4. Decision rule uncertainty arises whenever there is

ambiguity or controversy about how to quantify or compare social objectives. Examples include choosing an

appropriate measure to describe risk, choosing a summary statistic to characterize uncertain risk, choosing

parameters that define acceptable risk (such a one in

million chance) or the monetary value of risk (the value

of a life saved or a disease avoided), choosing a summary statistics, choosing a social welfare function, and

choosing a method for trading off immediate versus

delayed consequences.

In LCA a decision rule specifies how impacts should be

assessed (selection of assessment method, choice of

a specific endpoint, etc.). Decision rule uncertainty

arises when there is ambiguity about the goals of the

analysis. For example, HTPs are based on the product

of toxic potency and potential dose. Potential dose in

models like CalTOX can be assessed using different

specifications of removal of the pollutant. In a closed

system, no advection is allowed and the entire life cycle

of the pollutant is tracked by the model. In an open

system, advection is allowed and the assessment is

closer to a region-specific assessment. This difference

in boundary conditions can have a large influence on

the HTP values, especially for persistent pollutants such

as TCDD.

References

Baird, S. J., J. T. Cohen, J. D. Graham, A. I. Shlyakheter and

J. S. Evans (1996). “Noncancer Risk Assessment: A

Probabilistic Alternative to Current Practice.” Hum. Ecol.

Risk Assess. 2(1): 79-102.

Chiu, W., D. Hassenzahl and D. Kammen (1999). “A comparison of regulatory implications of traditional and exact twostage dose-response models.”Risk Anal. 19(1): 15-22.

Finkel, A. M. (1990). Confronting Uncertainty in Risk Management–A Guide for Decision-Makers. Washington, Resources for the Future.

Gaylor, D. W., J. J. Chen and D. M. Sheehan (1993). “Uncertainty in Cancer Risk Estimates.” Risk Anal. 13(2): 149-

154.

Hertwich, E. G. (1999). Toxic Equivalency: Accounting for

Human Health in Life-Cycle Impact Assessment. Ph.D.

thesis, Energy and Resources Group, University of California, Berkeley. 237 p.

Hertwich, E. G., T. E. McKone and W. S. Pease (in press).

“Parameter uncertainty and variability in evaluative fate

and exposure models.” Risk Anal.

Hertwich, E. G., W. S. Pease and T. E. McKone (1998). “Evaluating toxic impact assessment methods: What works

best?”Environ. Sci.Technol. 32(5): A138-A144.

Maddalena, R. L., T. E. McKone, D. W. Layton and D. P. H.

Hsieh (1995). “Comparison Of Multi-Media Transport and

Transformation Models - Regional Fugacity Model Vs

CalTOX.”Chemosphere 30(5): 869-889.

McKone, T. (1993). CalTOX, A Multimedia Total Exposure

Model for Hazardous-Waste Sites. Livermore, CA,

Lawrence Livermore National Laboratory http://

www.cwo.com/~herd1/caltox.htm

Hertwich’s Biography

Hertwich is a Ph.D. candidate in the Energy and Resources

Group (ERG) of the University of California, Berkeley. He

holds a BA in physics from Princeton University and an MS

in Energy and Resources. His work on the application of
36

multimedia, multiple pathway fate and exposure models lead

to the development of toxic equivalency potentials for cancer and noncancer effects. This weighting system is being

used by the Environmental Defense Fund's Scorecard project

[www.scorecard.org] to weight toxic emissions from all major

US manufacturing facilities.

Hertwich’s Overheads

A Framework for the Uncertainty

Analysis of the Human Toxicity

Potential

Edgar G. Hertwich

Thomas E. McKone

William S. Pease

University of California, Berkeley

http://greenmfg.me.berkeley.edu/~edgar

http://www.scorecard.org

Finkel’s Framework for Uncertainty

Analysis

Parameter uncertainty: random, systematic, and

measurement errors

Model uncertainty: due to simplifications , the use

of surrogates, errors in model form etc.

Decision rule uncertainty: ambiguity or

controversy about social objectives

Variability: natural variation of

input parameters

The Purpose of

Uncertainty Analysis

Uncertainty Analysis is required :

• to develop confidence in an analytical result

• as an input to formal decision analysis techniques,

and

• as a tool to refine impact

assessment methods.

Uncertainty analysis is a tool for recognizing how prudent or imprudent proposed

actions may be in the face of incomplete knowledge, and for modifying control

and research decisions accordingly. (Finkel, p. 39)

Model Uncertainty

Case: Plant Compartment

Alternative models for the interaction between plant

and air, and their effect on human exposure, as well

as on the concentration in the plants.

0.0

0.2

0.4

0.6

0.8

1.0

1 E-3 1 E-2 1 E-1 1 E+0 1 E+1

modified/original plant compartment

 Concentration 

 in plants

Exposure

Model Uncertainty

Case: Continuous Rain?
37

Model Uncertainty

Case: Continuous Rain?

1 E-12

1 E-9

1 E-6

1 E-3

1 E+0

1 E-12 1 E-10 1 E-8 1 E-6 1 E-4 1 E-2 1 E+0

Hazard index 'no rain' 

regular vs no rain

mod. H vs. no rain

mod. H vs. regular

Parameter Uncertainty

Chemical Specific Parameters

Variability

Landscape-Specific Parameters

Error bars indicate chemical-specific parameter uncertainty

Conclusions

• Finkel's framework is useful for organizing

uncertainty

• Uncertainty Analysis is exploratory and

never complete

• Some uncertainties are significant, others

are negligible

• This type of analysis provides useful insight

into the power and limitations of HTP

• Uncertainty analysis can help to develop

and refine analytical decision-support tools.
38

Life Cycle Impact Assessment (LCIA) aims to assess

the changes in the environment which are judged to be disadvantageous, i.e., damages to safeguard subjects (representatives of the environment). The environmental system

as such can be best described as overcomplex (Berg et al.

1994). The identification of disadvantageous changes and

of safeguard subjects are inherently value-laden steps. Sophistication in LCIA methods means therefore not just to be

more detailed or comprehensive in modeling but as well to

pay attention to the different levels of uncertainties by the

use of appropriate methods.

Van Asselt et al. (1996) identified a similar situation in

integrated assessment and chose to use a typology of uncertainties based on Funtowicz et al. (1989) (see typology

in the annex). In LCIA we can distinguish three levels of

uncertainties which ask for sophisticated methods to deal

with them:

1. The environmental system is overcomplex, only some

causal relationships between environmental interventions and damages to safeguard systems can be established. Lack of understanding and disagreement

among experts lead to epistemological uncertainties.

2. Many steps in the assessment need value-laden judgements. e.g., the perception of the ecosphere or the time

preference are dependent on the individual world-view.

Subjectivity is therefore a further source of uncertainty.

3. The quantitative modeling of the ecosphere is partly

based on approximations using data with inherent variability and statistical variation. This leads to technical

and some methodological uncertainties.

Suggestions to deal with these three levels of uncertainty

will be given here. However, the focus will be on level 3

using the example of carcinogenic effects in humans. A

detailed description of these steps including data for respiratory and carcinogenic effects is available in Hofstetter

(1998). A more concise description of the damage assessment approach was published in Goedkoop et al. (1998).

1) Epistemological uncertainty

It is suggested to model the overcomplex ecosphere by

three submodels. The known damage is modeled by the

available knowledge on causal relationships between environmental interventions and damages to safeguard subjects.

This includes fate, exposure, effect, and damage analyses

(impact-pathway analysis) and requires that safeguard subject and damaging changes to safeguard subjects are defined beforehand. The unknown damage is a proxy for all

interactions in the ecosphere we do not know (yet). For the

time being it is suggested to use the ratio between the potential to accumulate and the present geogenic plus anthropogenic flows as characterization factors to construct the

proxy indicator. The third submodel addresses the manageability of damages that is operationalized by three indicators: Ease of damage reduction, excess of long-term policy

targets, success of regulation.

These three submodels permit inclusion not only of a vast

amount of information on causation but also of precautionary principles and dynamic aspects.

2) Subjectivity

It is suggested to model and manage the subjectivity rather

than to hide or separate it. Therefore, Cultural Theory (Thompson et al. 1990) has been chosen to model society.

Three of the five archetypes described by Cultural Theory

have been chosen for a sophisticated scenario generation

because of their active role in decision-making: Individualists, hierarchists, and egalitarians. The aim was to model

all value-laden sub-steps compatible with these cultural

perspectives, i.e., to come up with three instead of one

LCIA, each one being based on a consistent set of value

judgements.

In the case of the three submodels for the ecosphere this

means that individualists will place most importance on the

submodel known damages while egalitarians will follow a

more precautionary approach and judge the submodel on

unknown damages to be the most important.

Tumors at different cancer sites give rise to individual

histories of disease and lead - to some extent - to premaThe Different Levels of Uncertainty Assessment in LCIA;

The Case of Carcinogenic Effects

Patrick Hofstetter, Env. Sciences:

Natural and Social Science Interface, ETH Zürich
39

ture death at different ages. The years life lost (YLL) per

cancer site and the average duration and severity of diseases (leading to the years lived disabled (YLD)) can be

aggregated to disability adjusted life years (DALYs) (Murray

et al. 1996). To do so a number of value choices are necessary on equity between races, age, and social status, on

time discounting, and on the appropriate procedures to estimate the disability weights. This being a typical example

where value choices are needed. World-view dependent

judgements on these factors are summarized in the appendix.

3) Technical and some methodological uncertainties

The damage assessment of substances causing carcinogenic effects in human beings is an illustration of how to

deal with methodological and technical uncertainties in quantitative modeling (see appendices). First of all it is assumed

that point estimates have to be supplemented by information on the probable distribution. For reasons of simplicity it

is assumed that all parameters are lognormally distributed

and that the 95% confidence interval is described by the

squared geometric standard deviation s2

. The tables and

figures in the appendix indicate the impact pathways for

damages to human health and illustrate how the uncertainties have been dealt with.

Most of the fate factors (F) have been modeled using a

multi-media steady-state model. The accuracy of the model

outcome depends on the quality of the input parameters,

the suitability of the model for the substance to be modeled, and by the ways substances are distributed and partitioned. Using these factors, uncertainty classes for the substances at hand have been defined and geometric standard

deviations estimated. In the case of heavy metals dry and

wet deposition velocities from literature were used to make

more accurate estimates for the air to air fate factors, which

leads to much smaller geometric standard deviations than

with the fugacity model. The fate factors for heavy metals

and some other substances that do not conform to the limitations of fugacity models should be further improved by

using other models for intake by drinking water and food.

The exposure of human beings is dependent on many

factors and shows high variability. One of the more decisive

factors is the population density. Assuming that an emission occurs in Europe and an average wind speed of 5 to 10

m/s, one can assume that substances with high atmospheric

residence times will be distributed over a large area including less densely populated areas in Asia and the sea. Taking into account the geographical situation, the population

densities in the concerned countries and a maximum population density at the emission source results in the graph

included in the appendix. A substance-specific population

density has been attributed to each substance. This procedure significantly reduces the uncertainty in the exposure

assessment.

Effect data for carcinogenic substances is available from

epidemiological studies, bioassays and other toxicology

studies. The International Agency for Research on Cancer

(IARC) groups the substances according to the strength of

evidence for their causing of cancer in human beings (Group

1: highest evidence). The selection of the IARC-Groups to

be included in the analysis can not be based on scientific

reasoning and has nothing to do with the quality of the doseresponse factors. Cultural Theory is used here to select the

groups that fit best with each of the three cultural perspectives, i.e., it is treated like a question of value choices (level

2). In addition to this epidemiological uncertainty there is

the uncertainty due to variability and approximation. The

extrapolation to low doses and the transferability from animals to humans are major uncertainty factors. Thanks to

some epidemiological studies (mostly for inhalation) for

substances subsumed under Group 1 and 2, one can assume that the geometric standard deviations is lower for

substances with higher evidence and for the inhalation.

The following tables show the aggregation of all the

substeps of the fate, exposure, effect and damage analyses for some carcinogenic substances of IARC-Group 1. It

can be seen that the geometric standard deviations are

especially large for the heavy metals which poorly fit with

the fate model used. The figure with error bars in the appendix represents the results of the damage assessment for a

set of 55 carcinogenic substances that are emitted to air,

including all the exposure routes. The 95% confidence interval spans 1.5 to 5 orders of magnitude. However, the

highest and the lowest mean for a substance show a difference of eleven orders of magnitude, i.e., the uncertainty is

reduced from eleven to at least five and in many cases to

two or three orders of magnitude.

An additional reduction of uncertainty is possible thanks

to the three distinct LCIA models for each of the three cultural perspectives. In the case of a one-score eco-index for

a specified product system, the probability distribution for

just one cultural perspective will be much narrower than the

value-independent probability distribution for all combinations of value choices.

The three levels of uncertainty discussed each asks for a

different tool to deal with it. The solutions presented here to

deal with these uncertainties will enhance the sophistication of LCIA, on the one hand, and make LCIA results more

valuable as decision support, on the other hand. This comprehensive modeling approach may well lead to a robust

LCA that is simple to use in practice.

References

Berg M., M. Scheringer, Problems in Environmental Risk Assessment and the Need for Proxy Measures, Fresenius

Environmental Bulletin 3 (8): 487-92 (1994)

Funtowicz S.O., J. Ravetz, in: Les Experts sont Formels:

Controverse Scientifiques et Décisions Politiques dans

le Domaine de l’Environnement, Arc et Sanas, France

1989

Goedkoop M., P. Hofstetter, R. Müller-Wenk, R. Spriensma,

The Eco-Indicator 98 Explained, Int. J. LCA 3 (6) 352-

360 (1998)
40

Hofstetter P., Perspectives in Life Cycle Impact Assessment; A Structured Approach to Combine Models of

the Technosphere, Ecosphere, and Valuesphere, Kluwer

Academic Publishers, Boston 1998

Murray Ch.J.L., Lopez A.D. (Eds.), The Global Burden of

Disease, Volume I of Global Burden of Disease and

Injury Series, WHO / Harvard School of Public Health /

World Bank, Harvard University Press, Boston 1996

Thompson M., Ellis R., Wildavsky A., Cultural Theory,

Westview Print Boulder 1990

van Asselt M.B.A., A.H.W. Beusen, H.B.M. Hilderink, Uncertainty in integrated assessment: A social scientific

perspective, Environmental Modeling and Assessment

1 (1996) 71-90

Hofstetter’s Biography

Hofstetter, 1989, graduate degree in mechanical engineering ETH Zürich; 1989 LCA of light bulbs including an evaluation of methods for LCIA; since 1989 own consultancy “Büro

für Analyse & Ökologie” in the field of industrial ecology,

processes for waste treatment, LCA, and eco-design. 1990-

1994 research for the report and database “Ökoinventare

für Energiesysteme” (Life Cycle Inventories for Energy Systems), at the Energy Systems Laboratory, ETH Zürich. Since

1994 member of the LCA-Steering Committee of SETACEurope; 3. - 6. 1994 research stay at the Centruum voor

Milieukunde (CML), University of Leiden; since 1994 research

on Life Cycle Impact Assessment. 1998 Ph.D. thesis on

“Perspectives in Life Cycle Impact Assessment; A Structured Approach to Combine Models of the Technosphere,

Ecosphere, and Valuesphere” at the Chair of Environmental

Sciences: Natural and Social Science Interface, ETH Zürich.

Since September 1999 he is an ORISE research fellow at

U.S. EPA, Cincinnati and visiting scientist at Harvard School

of Public Health, Boston.
41

Eidgenössische

Technische Hochschule

Zürich

Ecole polytechnique fédérale de Zurich

Politecnico federale di Zurigo

Swiss Federal Institute of Technology Zurich

An International Workshop on

Life Cycle Impact As s es s ment Sophis tication

Brussels 29-30 November 1998

Co-organised by UNEP, U.S. EPA, and CML Leiden

THE DIFFERENT LEVELS OF UNCERTAINTY ASSESSMENT IN LCIA:

THE CASE OF CARCINOGENIC EFFECTS

Patrick Hofstetter,

Environmental Sciences: Natural and Social Science Interface (UNS)

Swiss Federal Institute of Technology (ETH Zurich)

hofstetter@uns.umnw.ethz.ch

Multifactor interaction network in carcinogenesis based on some of the major

relationships identified (dashed double lines that break an arrow symbolise

inhibition or blockage of the path) (Arcos et al. 1995)

How to deal with main problems in LCIA?

Environmental s ys tem and its caus al relations hips are overcomplex

=> As s es s ment has to reflect epis temological uncertainty

(known damage, unknown damage, manageability)

Perception of the ecos phere and its behaviour is dependant on the

pers pective

=> As s es s ment s hould cope with this s haped perception

(s ubjectivity)

(different models for different perspectives, Cultural Theory)

Modelling is hampered by technical and methodological uncertainties

=> Us e quantitative uncertainty as s es s ment to cope with

technical uncertainties and us e more than one method to

reduce methodological uncertainty

(Monte Carlo Simulation, combination of descriptive AND

prescriptive approaches)

Acceptable 

damage 1.

2. 3. Manageability Unknown damage 

Unknown causalities

Damage 

 due to known 

causalities

The modelling framework for the ecosphere (the arrows – the third modelling

level of 'manageability' – symbolise the dynamic aspects of a damage

reduction towards an acceptable damage).

Acc eptable 

dama ge 1.

2. 3.Man ageab ility Unkno wn da ma g e 

unknown c ausa litie s

Damag e 

 due to kno wn 

caus alities

Ac ce ptable 

damage 1.

2. 3. Man ageab ility

Unknown damag e 

unknown ca us alities

Damag e 

 due to kno wn 

caus alities

Acc eptable 

dama ge

1.

2.

3. Man ageab ility

Unkno wn dam ag e 

unkno wn caus alities

Damag e 

 due to kno wn 

caus alities

Individualis t's m odel o f the eco s phere Egalitarian's mo del of the ec os phere

Hiera chis t's mo del o f the eco s phere

?

Fig. 4.11: The relevance of the three submodels for the ecosphere judged

from the viewpoint of the three active perspectives. The question mark

indicates that fatalists need not to be considered here because they are no

active decision makers.

Inventory Analysis 

Fate and Exposure 

Analyses 

Effect Analysis 

Damage Analysis

Emissions 

Exposure 

Effects on Single Endpoints 

Damage to Safeguard 

Subjects

Technosphere Ecosphere

Empirically derived fate factors

Models

Single compartment 

Multi-media

Steady state 

Dynamic

Steady state 

Dynamic

Toxicological tests, laboratory experiments

Epidemiological studies

Ecological studies 

Cohort studies 

Cross-sectional studies

Expressed preferences 

Revealed preferences

Contingent valuation 

Panel methods 

...

General set-up of the impact pathway analysis within the ecosphere. The

upper arrows for each level of analysis give examples for the cause-oriented

modelling and the lower arrows specify the descriptive modelling

Hofstetter’s Overheads
42

Empirical data to reduce uncertainty of fate factors for heavy metals

Dry deposition

velocity Vp

(Pacyna 1995)

Fair,air (dry) Dry deposition

velocity Vp

(Pacyna et al.

1985)

Fair,air (dry) Fair,air (tot) σg

2

cm/s m2

a/m3 cm/s m2a/m3 m2a/m3 -

(1) 100/((1)·365·24·

3600)

(2) 100/((2)·36

5·24· 3600)

100/(((2)+0.2)·

365·24·3600)

Arsenic 0.1-0.4 32 E-6 - 8 E-6 0.2 16 E-6 8 E-6 4

Nickel 0.4-2 8 E-6 - 1.6 E-6 0.5 6 E-6 4.5 E-6 4

Chromium 0.3-0.9 11 E-6 - 3.5 E-6 0.4 8 E-6 5.3 E-6 4

Cadmium 16 E-6 8

Calculation of the fate factors F air,air from the deposition velocity. The

wet deposition velocity is calculated by the ratio of the assumed dilution

height of 800 m with the residence time due to wet deposition (τwet = 5

days) resulting at about 0.2 cm/s.

Uncertainty classes for different substance classes

Criteria Substances concerned

(for emission to air)

σg

2

F'' air,

air

σg

2

F'' air

,water

σg

2

F'' air,

food

• More than 95% of the total

intake is inhaled, i.e., the

substance shows only minor

partitioning

• The properties are well known

• The substance is well suited

for the chosen model

Methyl chloride, 1,1,1,2-Tetrachloroethane, 1,2-

Dichloroethane, Chloroform, Dichloromethane, 1,2-

Dibromoethane, Hexachloroethane, 1,1,2-Trichloroethane,

Perchloroethylene, Bromodichloromethane, Ethylene oxide,

Benzene, Benzyl-chloride, Trichlorethylene, Vinyl chloride,

Acrylonitrile, Epichlorhydrin, Diesel soot particles, 1,1

Dichlorethylene, Carbon-tetrachloride, Styrene, BCEE,

BCME, Acetaldehyde, 1,3-Butadiene

248

• The substance partitions

• The properties are well known

• The substance is well suited

for the chosen model

Trifluralin, Hexachlorobenzene, alpha-HCH, Dichlorvos,

DEHP, Dibutyl-phthalate, beta-HCH, Formaldehyde,

Aldrin, gamma-HCH, 1,4-Dioxane, PCBs,

Benz[a]anthracene, BaP, Dieldrin, 3-Methylchloanthrene,

TCDD, Dibenz[a,h]anthracene

3 6 12

• The properties are uncertain or

unknown, or

• The substance is not well

suited for the chosen model

Arsenic, Cadmium, Nickel, Nickel-ref.-dust, Nickelsubsulfide, Chromium(VI), Propylene-oxide, 1,1,2,2-

Tetrachloroethane, Benzotrichloride, Hexachlorobutadiene,

2,4,6-Trichlorphenol, Pentachlorphenol

20

(§)

40 80

Substance-specific population density

0

0.00005

0.0001

0.00015

0.0002

0.00025

0.0003

0.00035

0.0001 0.001 0.01 0.1 1

Atmospheric residence time in air [years]

Population density [persons/m2]

Population density as a function of the atmospheric residence time of substances emitted

in Western Europe according to Equation (6.12).

IARC - Groups of evidence: Epistemological and methodological

uncertainties

Cultural

Perspective

Attitude and Argumentation Choice

Individualist The individualist prefers an adaptive management style which means that resources

are spent for proven harm to human health at the moment of decision. The decision

will immediately be adjusted if new evidence emerges so as to realise an optimal

resource allocation. Risks which have not yet resulted in evident harm are accepted.

Such an attitude makes it possible to consider Group 1 substances only because of the

limited evidence for all other substances.

Group 1

Hierarchist The hierarchist's management style is to control situations. He is therefore closest to

the authoritative bodies in government and in international organisations. The

responsibility for the group and towards the leader of the group demands finding a

balance between evidence and probability. WHO (1987, 1996a) gives in its guidelines

risk estimates for a small selection of Group 1, 2A and 2B substances. Substances

from Group 3 are not included because this could lead to an unjustified cut-back of

needs already expressed by society.

Groups

1, 2A,

2B

Egalitarian The risk aversive and preventive management style asks for an attentive attitude

towards risk management. Egalitarians are well aware that many substances in the

inclusive Group 3 might in the coming decades be proven to be potent human

carcinogens. The responsibility towards the others in the group (and coming

generations) leads to the inclusion of all risk factors. Egalitarians may be ready to

reduce the satisfaction of their needs in order to spend more resources on risk

prevention than individualists or hierarchists would accept (however this is here not

the important point).

Groups

1-3

Variability and approximation of unit risk factors

IARC-Group σg

2 for inhalation σg

2 for drinking water and oral intake

12 4

2A 3 6

2B 5 10

3 10 20

Main questions: Extrapolation to low doses

Transferability from animals to

humans

Epidemiological studies are important for validation

Estimated geometric standard deviations (lognormal

distribution) for the unit risk factors given in

Appendix (6.7).

Variability and approximation of the damage analysis by

Disability Adjusted Life Years (DALYs)

Cancer sites YLL m(0,0) Average

disability

weights Dm

(Murray et

al. 1996a)

Average

duration of

disease Lm

(Murray et

al. 1996b)

YLDm (0,0) DALYs m(0,0) σg

"

Lung 15.8 0.146 2 0.29 16.1 1.5

Skin melanoma 3.2 0.045 4.4 0.20 3.4 1.5

Nasal, nose, nasopharynx 12.1 0.096 4.3 0.41 12.5 2

Liver 15.8 0.239 1.77 0.42 16.2 1.5

Leukaemia 16.0 0.06 3.8 0.45 16.5 1.5

All sites 12.6 0.154 3.4 0.48 13.1 1.5

Main problems: • Equity problems in general

• Time discounting

• Age-weighting

• Disability weights
43

Discounting and age-weighting in the 

DALYs-concept per cultural perspective

Individualist Egalitarian Hierarchist

Discount rate derived from Cultural Theory 5% 0% 2%

Discount rate due to LCA sensu stricto restrictions 0% 0% 0%

Age-weighting derived from Cultural Theory yes no no

Multiplying Lognormal Distributions

μg= eμ μg = geometric mean, i.e., expectation value

σg = eσ σg = geometric standard deviation

The product of n independent lognormally distributed random variables Xj j = 1...n

with parameters (μgj, σgj) j = 1...n is lognormally distributed with

expectation value

μg tot = Π(j=1..n μgj and

geometric standard deviation

σgtot = e

Σ

j=1

n

log2

( ) σgj

Composition of all elements in the Impact-Pathway Analysis

(Uncertainties in quantities)

Substance-name σg

2

DALYs air,

inhalation

σg

2

DALYs air,

drinking water

σg2

DALYs

air,food

σg2

DALYs water,

inhalation

σg2

DALYs water,

drinking water

σg2

DALYs

water,food

IARC-Group 1 - - - - - -

Arsenic, inorganic 7.0 61.0 114.8 51.0 33.2 114.8

Benzene 4.6 9.8 15.8 9.5 8.3 21.7

Bis(chloromethyl)ether BCME 4.6 9.8 15.8 9.5 8.3 21.7

Cadmium 7.0 48.0 229.5 51.0 24.9 229.5

Chromium(VI) 7.0 48.0 229.5 51.0 24.9 229.5

Nickel 7.0 48.0 229.5 51.0 24.9 229.5

Nickel-refinery-dust 7.6 49.9 236.2 53.1 26.2 236.2

Nickel-subsulfide 7.6 49.9 236.2 53.1 26.2 236.2

Vinylchlorid 4.6 33.2 46.1 9.5 29.9 58.2

Compilation of the geometric standard deviations of all elements in the Impact-Pathway

Analysis

Substance-name σg2

F''air,a

ir

σg2

F''air,

water

σg2

F''air,

food

σg2

F''

water,

air

σg2

F''water,

water

σg2

F''water,f

ood

σg

2

ρ

σg2

E inhalation

σg2

E oral

intake

σg2

E

drinking

water

σg

2

DALYs

IARC-Group 1 - - - - - - - - - - -

Arsenic, inorganic 4 40 80 40 20 80 3 2 4 4 1.5

Benzene 2 4 8 6 3 12 3 2 4 4 1.5

Bis(chloromethyl)ether BCME 2 4 8 6 3 12 3 2 4 4 1.5

Cadmium 4 40 80 40 20 80 3 2 20 1 1.5

Chromium(VI) 4 40 80 40 20 80 3 2 20 1 1.5

Nickel 4 40 80 40 20 80 3 2 20 1 1.5

Nickel-refinery-dust 4 40 80 40 20 80 3 2 20 1 2

Nickel-subsulfide 4 40 80 40 20 80 3 2 20 1 2

Vinylchlorid 2 4 8 6 3 12 3 2 20 20 1.5

0.000000001

0.00000001

0.0000001

0.000001

0.00001

0.0001

0.001

0.01

0.1

1

10

100

1000

10000

100000

1000000

Arsenic

Benzene

Bis(chloromethyl)ether

Cadmium

Chromium(VI)

Nickel

Nickel-refinery-dust

Nickel-subsulfide

Vinylchlorid

1,2-Dibromoethane

1,3-Butadiene

Acrylonitrile

Benzo[a]pyrene

Benz(a)anthracene

Dibenz(a,h)anthracene

Diesel soot particles

Epichlorhydrin

Ethylene Oxide

Formaldehyde

PCBs

Propylene-oxide

1,2-Dichloroethane

1,4-Dioxane

TCDD

2,4,6-Trichlorophenol

Acetaldehyde

a-Hexachlorcyclohexan

Benzotrichloride

Benzyl-chloride

beta-Hexachlorcyclohexan

Bromodichloromethane

Carbon-tetrachloride

Chloroform

Di(2-ethylhexyl)phthalate

Dichloromethane

Dichlorvos

gamma-Hexachlorocyclohexane

Hexachlorobenzene

Pentachlorophenol

Perchloroethylene

Styrene

1,1,1,2-Tetrachlorethane1,1,2,2-Tetrachlorethane

1,1,2-Trichlorethane

1,1-Dichlorethene

3-Methylcholanthrene

Aldrin

Bis(2-chlorethyl)ether

Dibutyl-phthalate

Dieldrin

Hexachlorobutadiene

Hexachlorethane

Methyl Chloride

Trichlorethylene

Trifluralin

Eco-Index

Probability of value

Individualist

Hierarchist Egalitarian

Probability 

distribution 

considering all 

combinations
44

Conclus ions

• Dealing with overcomplex and ill-defined systems asks for:

- tools dealing with technical, methodological, and epistemological

 uncertainties

- not aiming for world-models but sufficient information for ranking of

 alternatives, i.e., most important impact pathways are mostly sufficient

• Present knowledge on causal relationships between interventions and

damages shall be compiled and harmonised (and supported by proxy

indicators)

• This tremendous work will allow for a feedback to science policy and

priorisation in the research of the concerned disciplines

• The turn-around via increased sophistication will allow for simple to use

and robust LCIA

• Quantitative uncertainty assessment allows to identify non-significant

differences between product alternatives (important finding)
45

Introduction

In order to avoid the present possibility to choose an impact assessment method in order to obtain a desired result,

there is an urgent need for criteria to select consistent impact assessment methods. The present paper aims at the

following objectives:

- to define criteria for consistent effect, fate and exposure analysis,

- to identify key parameters for the fate assessment and

to analyze different fate models on the basis of these

parameters,

- to discuss the possibility of overall experimental checks

and finally,

- to conclude on modeling versus scoring approaches.

Framework for Fate and Exposure in

Toxicity: Consistency Requirements

The first SETAC-Europe working group on Life Cycle Impact Assessment (Jolliet et al., 1996) developed a framework for human and ecotoxicity where characterization factors are expressed as the product of an effect factor times

a fate factor (fig. 3). This framework also provided a consistency principle: the Life Cycle Impact assessment method

performs a full fate and exposure analysis if the fate factor

F effectively relates the inventory emission to the chosen

toxicity effect factor (fig. 4). For instance, if a Reference

Dose is used as the toxicity reference, then the emission

should be related to the amount of pollutant effectively inhaled or ingested.

Identification of Key Parameters for

Exposure Efficiency

Jolliet and Crettaz (1999) introduced the concept of human exposure efficiency in LCIA: retaining a dose as toxicity reference, the consistency principle means that the

fate factor for human toxicity can be described as the overall fraction of an emission which is taken in (inhaled or

ingested) by all human beings over the lifetime of a

substance (fig. 3):

Evans et al. (1997) used the same concept for risk assessment, calling the ratio of total human intake to total

emissions the “exposure efficiency” (e).

Jolliet and Crettaz (1999) also developed an analytical

and modular approach to calculate exposure efficiency (fig.

5). The new model is developed on the basis of a modular

approach which offers an alternative way of simplification

than the traditional Mackay I, II and III classification. A summary of this calculation is provided in the appendix. The

approach allowed characterization of the fate and exposure

in each media using the five following key parameters (boldfaced):

a) in a first step, fate and exposure is calculated for each

media, assuming that the concentration in other media is

null. Concentration increases are proportional to resident

times divided by the height of dilution. To obtain exposure, these factors are then multiplied by the total amount

of air, water and food ingested annually by humans and

by bio-concentration factors for food exposure.

b) In a second step, the different compartments are connected on the basis of inter-media transfer factors (fraction of the emitted substance in medium m that reaches

medium n)

c) It becomes possible to take into account the exposure

to daughter degradation product of a substance, introducing an inter-substance transfer factor: the mass of daughter degradation product j per unit mass of substance i initially emitted

Analysis of Different Fate Modeling +

Scoring

a) No fate analysis

Most of the methods applied in life cycle assessment

until recently did not consider the “fate behavior” of pollutHuman Toxicity and Ecotoxicity - Modeling Versus Scoring

O. Jolliet

Swiss Federal Institute of Technology Lausanne, Institute of Soil and Water Management

Ecosystem management, CH-1015 Lausanne EPFL

olivier.jolliet@epfl.ch, http://dgrwww.epfl.ch/GECOS/DD

Fi

mm

i

mm = = ε

mass of substancei emitted in media mand

taken in by mankind through pathway n

massof substancei emitted in media n
46

ants, i.e., they assumed that all substances have the same

fate properties (“critical volume”, CML 1992, EcoIndicator

95, etc.). This is clearly not a valid assumption, since routes

of exposure and residence times vary widely (up to factors

of 10,000).

b) Partial fate analysis: scoring

The EDIP method (Hauschild and Wenzel, 1997) performs

a “partial fate” analysis. Fate is incorporated through a biodegradability factor based on OECD biodegradability tests,

varying from 0.2 for biodegradable substances to unity for

persistent substances. This allows coverage of a large number of substance, but does not satisfy the consistency requirements of fig. 4. Ranking methods based on Persistence-Bioaccumulation-Toxicity information could be interesting to screen and select pollutants; however such coefficients cannot usually be directly multiplied by the mass of

the environmental interventions, as required for LCA (Udo

de Haes et al. 1999). It is suggested that this information

can be used to extrapolate the key parameters described in

fig. 5 and appendix 1. Figure 8 presents how half-life in soil

could be extrapolated based on OECD biodegradability tests.

c) Full fate and exposure analysis using the concept of

exposure efficiency

So far, determination of fate coefficients for toxicity characterization in LCIA has mostly been based on the principle of fugacity modeling (Guinée et al., 1996; Hertwich et

al., 1997; Goedkoop et al., 1998). Especially Mackay type

three models have been used in several calculations of characterization factors for human and ecotoxicity (fig. 9 to 11).

The applied models are rather comprehensive in the description of the exposure pathways, but have the drawback

of complexity linked to the integrated partitioning between

the different compartments. Indeed, a bias may be more

easily hidden in a more sophisticated approach. The number of parameters and equations to be solved simultaneously

make them very difficult to check against measured data or

even to check their order of magnitude (Berding et al., 1999).

Based on this concept of exposure efficiency, the comparison between two fugacity multi-media models is presented: errors and default data can be quickly identified and

corrected. Large variations of up to seven orders of magnitude are observed on the overall exposure efficiency. The

highest exposure efficiencies are observed for substances

that are ingested through the food pathway, the exposure by

inhalation being a factor smaller for these substances.

Residence times and exposure efficiencies calculated by

the EUSES and CALTOX models were compared (Jolliet et

al, 1998, fig. 10 and 11). On the one hand, figures 12 and 13

show that the use of default data for risk assessment cannot be used in LCIA, as best estimates and not safe values

are required. On the other hand, once errors or default data

are corrected, there is a good agreement in the exposure

efficiencies calculated by the two models, with a variation

of up to 8 orders of magnitude (fig. 13). Interestingly, the

highest exposures are observed for exposure by food ingestion.

d) an intermediary approach

The modular approach described by figure 5 and appendix 1 (Jolliet and Crettaz, 1999) offers an alternative to fugacity modeling (fig. 14 to 16). Each compartment is modeled

in an independent module and then connected to the other

compartments by inter-media transfer coefficients. This

enables each module to be validated against measurements

or even to shortcut some parts using empirical data. Because of its modular character, this approach also enables

to take into account the exposure to daughter degradation

product of a substance (appendix 1). Hauschild and Jolliet

(1999) have recently developed the air module that predicts

the atmospheric residence time and the fraction of the emission deposited (fig. 17). Checked against a number of measurements, there was a good correlation between predicted

and measured lifetimes in air (fig. 18: R2

=0.85). The intermittent character of rain plays an important role and a simple

model for intermittent wet deposition has been developed

for steady state modeling (fig. 19).

Effect Data for Human- and Ecotoxicity

For the analysis of effect coefficients, Hofstetter (1998)

recently proposed a first approach to determine Years of

Life Lost or Years of Life Disabled for carcinogenics and a

few other pollutants for which epidemiological data are available. Crettaz et al. (1999) proposes to use a slope based on

ED10, enabling a similar approach for carcinogenics and

non carcinogenic substances and avoiding the use of conservative estimates such as q1* for carcinogenics (fig. 20

and 21). Extrapolation procedure are being developed and

checked for substances with little information. Most of the

conclusion for human toxicity remains valid and can be

adapted to ecotoxicological damages, considering that human toxicity aims at the protection of the individual whereas

ecotoxicology aims at the species protection.

Need for Sophistication

Spatial differentiation can be introduced as modifiers of

the effect and fate factors (fig. 23 and 24), as a function of

geographical or background information. Rabl and Spadaro

(1998) provides a good example of exposure variations with

population density around the emission source or with stack

height. Differentiation is to be introduced only if significant

changes (e.g. more than a factor 5) are observed. If a fully

site-specific approach is necessary, an environmental impact assessment is preferred to LCIA.

Conclusion and Consistency Criteria

- Variations on exposure efficiency and fate are very large,

up to 106 ( 6 orders of magnitude )

- Consistency between effect and fate is a must! The

choice of the level of the effect factor depends on uncertainties.

- Exposure efficiency and overall residence times are

useful concepts as an intermediary between modeling

and scoring.
47

- Reasonable consistency between results of different

models is observed, once errors are identified. Model

comparison needs to be performed in a systematic

manner with checks against measurements.

References

Berding, V.; S. Schwartz; M. Matthies (1999). Visualization

of the Complexity of EUSES. Environmental Science

and Pollution Research 6: 37-43.

Crettaz, P., Brand, K., Rhomberg, L. and Jolliet, O., 1999.

Human health effects of carcinogenic compounds; an

application for LCIA. AIChE Presentation Record, October 99.

Evans, J.S., Thompson, K.M. and Hattis D., 1997. Exposure efficiency. Journal of the air and waste management association.

Goedkoop, M.J., P. Hofstetter, R. Müller-Wenk and R.

Spriensma, 1998: The Eco-Indicator 98 Explained. Int.

J. LCA 3 (6): 352-360.

Guinée, J., Heijungs, R., van Oers, L., van de Meent, D.,

Vermeire, T. and Rikken, M., 1996. LCA impact assessment of toxic releases. Generic modelling of fate, exposure, and effect on ecosystems and human beings with

data for about 100 chemicals. RIVM report, pp. 70.

Haushild, M. and Wenzel, H., 1997. Environmental assessment of products - Scientific background, Thompson

science, Chapman & Hall, New York, pp. 525.

Hauschild, M. and Jolliet, O., 1999. Determination of atmospheric residence times and transfer factors from air to

soil and surface water. Prepared for Chemosphere.

Hertwich, E., McKone, T. and Pease W., 1997. Hazard and

risk-based approaches to comparing toxic emissions.

IEEE international symposium on Electronics and the

Environment, pp. 261-266.

Hofstetter, P., 1998: Perspectives in life cycle impact assessment. A structured approach to combine models

of the technosphere, ecosphere and valuesphere.

Kluwer, Boston, Dordrecht, London.

Jolliet, O., 1996, editor. Impact assessment of human and

eco-toxicity in Life Cycle Assessment, in “Towards a

methodology for Life Cycle Impact Assessment” (Udo

de Haes ed.). SETAC, pp. 49-61.

Jolliet, O. and Crettaz, P., 1997: Calculation of fate and exposure coefficients for the life cycle toxicity assessment of air emissions. International Journal of LCA 2

(2): 104-110.

Jolliet, O., Crettaz, P., Hofstetter, P., Hertwich, E. and

Spriensma, R., 1998. Comparison and check of models to include fate and exposure in LCIA. Abstracts of

the SETAC-Europe annual conference, Bordeaux.

Jolliet, O. and Crettaz, P., 1999: Tox Mod: Modular Fate and

Modelling of exposure efficiency for the characterization of human toxicity in Life Cycle Assessment. Int.

Journal of Risk Analysis, submitted.

Rabl, A. and Spadaro, J.V., 1998 Estimates of real damage

from air pollution: site dependence and simple impact

indices for LCA. Abstracts of the SETAC-Europe conference Bordeaux, May 98.

Udo de Haes, H.A., O. Jolliet, G. Finnveden, M. Hauschild,

W. Krewitt and R. Mueller-Wenk, 1999: Best available

practice regarding impact categories and category indicators in Life Cycle Impact Assessment. Report of second working group on life cycle impact assessment of

SETAC-Europe.

Appendix

A Modular Approach to Calculate Fate and

Exposure Efficiency

Jolliet and Crettaz (1999) also developed and presented

in detail an analytical and modular approach to calculate

exposure efficiency (fig. 5). The new model is developed on

the basis of a modular approach that offers an alternative

way of simplification than the traditional Mackay I ,II and III

classification:

a) in a first step, exposure is calculated for each media,

assuming that the concentration in other media is null.

Three issues are successively addressed:

a1) From emission to concentration: assuming steady

state, the increase in concentration can be calculated as the initial emission flow per unit area multiplied by the ratio of two key parameters: the overall

residence time (ti

m, yr) divided by the volume of

dilution per unit area (Vi

m, m3

/m2) of the medium

that contains the pollutant, per unit area:

[kg/m3] (1)

This ratio corresponds to the intuitive deduction that the

longer the residence time, the higher the concentration increase. The overall residence time is a combination of the

different removal processes: both the degradation of the

substance and its removal by transfer to another medium

on the one hand, and its advection on the other hand.

a2) From concentration to direct intake: Direct intake is

directly proportional to the time integrated concentration increase in air (resp. water and soil) multiplied by the overall volume of air inhaled (resp. water

or soil ingested) annually by humans per unit area.

[-] (2)

 Where rp is the population density [pers/m2

] and Vm in the

yearly intake of air, water or soil particles per person and

δ τ C

V i

mm i

m

i

m i

m = M

ε

τ

i ρ mm i

m

i

m p

food

V

= Q
48

per year [m3/pers-yr]. Depending on local characteristics

(e.g. air emission in high or low populated area), exposure

varies typically by a factor of 10 to 30 (Rabl and Spadaro,

1998). This is relatively restricted compared to the total

variation in exposure efficiencies of up to 8 orders of magnitude between different substances (fig. 13). It could therefore be addressed through generic spatial differentiation (e.g.

scenarios for emissions in high, mean and low populated

areas).

a3) Bioconcentration and indirect intake through the diet:

For all media, it is possible that an increase in the

concentration in the media will lead to an increased

concentration in food products (plants, fishes), that

eventually will be ingested by humans. The resulting

exposure efficiency through the diet is very similar to

that for direct intake: only here, the daily intake of the

surrounding medium is replaced by the food intake

and multiplied by the bioconcentration factor:

[-] (3)

 Where BCFi

m food is the bioconcentration factor: concentration in fish or plant divided by medium concentration [kg/

kg], rm is the density of medium m [kg/m3] and Q f in is the

yearly intake of food per person [kgf

/pers-yr]

This exposure pathway dominates for persistent substances such as heavy metals. In that case better estimates are required both for bioconcentrations and food intakes.

 b) In a second step, the different compartments are connected on the basis of inter-media transfer factors: The

emission of a substance into the initial medium m also

contributes to the concentration increase in the neighbor compartment n. This effect is calculated using the

inter-media transfer factor, that is, the fraction of the

emitted substance in medium m that reaches medium

n ( i

mn):

[kg /m3] (4)

 Similarly the exposure efficiency for inter-media transfer from medium m (e.g., air) to medium n (e.g., water) is

given by:

[-] (5)

 The inter-media transfer factors also enable the correction of the initial hypothesis of a null concentration in other

media. To the initial emission, an additional feedback emission must be added equal to the multiplication of two intermedia transfers. Therefore overall concentrations and total

exposure efficiency of an emission in media m, through

pathway m are equal to:

[-] (6) and (7)

The second order corrective term remains generally restricted as at least one of the intermediary transfer factors

is small.

Finally, this modular approach enables to take into account the exposure to degradation product j of the substance

i, introducing the inter-substance transfer factor fij

m, defined as the ratio of the mass of daughter degradation product j per unit mass of initial emission i:

[-] (8)

Jolliet’s Biography

Jolliet is assistant professor in sustainable development

at the Swiss Federal Institute of Technology Lausanne

(EPFL). Vice-chairman of the SETAC-Europe working group

on impact assessment, his research activities in LCIA focus on a modular approach to the modeling of fate and exposure for human and ecotoxicity. Ph.D. in Physics at the

EPFL (1988-CH), Postdoc at the Silsoe Research institute

(1989-GB), Pioneer work on LCA in agriculture at the FAT

(1991-CH), lecturer on LCA at the EPFL (1993-CH) and visiting scholar at the MIT (1997-USA).

ε

τ

ρ

i ρ m food i

m

i

m

m food

m p

food

V

BCF = Q

δ τ C

Vi f M n i

n

i

n i

mm

i

m

ε ε i

mm

i

mm

i

nn = f

δ τ

ε ε

C f

V

M

f f

i

m

i

nm

n

i

m

i

m i

m

i

m imm imm imm

n

= +

= +

∑

∑

( ) []

( )

1

1

Σ kg / m and 3

ε ε ij

m

ji

m

j

m = f

f
49

Jolliet’s Overheads

O.Jolliet, 3/11/99

Human toxicity Human toxicity and ecotoxicity and ecotoxicity

Modelling Modelling vs scoring

1. Introduction

2. Framework for fate and exposure in toxicity

3. Analysis of different fate modelling + scoring

4. Enabling experimental shortcuts and check

+ robust extrapolations

5. Conclusion and consistency criteria

Prof O.Jolliet (olivier.jolliet@epfl.ch)

EPFL-Swiss Federal Institute of Technology-Lausanne

Institute of Soil and Water Management

IATE-HYDRAM, CH-1015 Lausanne

Tel. +41 21 693 70 11, Fax +41 21 693 70 84 O.Jolliet, 3/11/99

Tell me your results

I will tell who paid you !

Urgent need for recognised and consistent

impact assessment methods

+ address ISO 14042 requirements

(scientifically valid + environmentally relevant)

--> needs for a general framework for toxicity

assessment.

No assessment means an implicit assessment !

Challenge

O.Jolliet, 3/11/99

E : effect factor F : fate factor

2.Framework for toxicity characterisation

(SETAC working group on toxicity assessment toxicity assessment:1996)

Si = Ei Fi Mi

links emission-effect

neglected until 1994 !

Toxicity measure

Si = εi Mi RfDi

1

RFDi , [kg/pers-an]

Reference dose

 εi , [-] “exposure efficiency”

 = fraction of the emission

taken in by mankind

Human

toxicity:

O.Jolliet, 3/11/99

Fate factors required for a full

fate and exposure analysis

NEC, NOEC,

Concentration

Emission to

concentration

increase

Effect

coefficient

Effect

variable

Fate

factor

required

ADI, RfD

Dose

Emission to

intake

dose

Acceptable

deposition rate

Deposition

rate

Emission to

deposition

O.Jolliet, 3/11/99

Fate: analysis of existing methods existing methods

No fate, No effect: only emissions, E=1, F=1

Only effect: critical volumes or analog

(Tellus 92, BUWAL, CML 92, ): F = τ / V = 1

Compares an emission with a concentration limit !

Pollutants with short lifetimes are overestimated

Partial fate: EDIP method (Haushild, Denmark),

F=Biodegradibility factor equal to 0, 0.5 or 1;

Problem of consistency

O.Jolliet, 3/11/99

Modelling Modelling versus scoring scoring

- Consistency between fate and effect is a priority

- Important to have coefficients multiplicative with

mass

- Simple ranking is not sufficient: in LCA it matters

if it is 5 times or 10'000 times higher

- Use Persistence - Bioaccumulation - Toxicity

informations to extrapolate key parameters in a

consistent fate & effect assessment
50

O.Jolliet, 3/11/99

Full fate: Modelised approach

fugacity modelling

 Inter-media Complexity Accuracy

1 Box model No low low

Mackay 3 yes high ???

CML-RIVM, 1996:USES / Hertwich, 1997:CALTOX

- inter-media transport included

- beware default data,

- little check against measured data

- height of dilution fixed to 1000 m3/m2

O.Jolliet, 3/11/99

Fate modelisation: intermediary level intermediary level

Challenge: Simple and robust+accurate approach !

Idea: introduce a double level

- Detailed modelisation, description

 of physico-chemichal phenoma,

 laboratory experiments (>150 paramètres)

- intermediary modelling based on a limited number

of key parameters, according to physics&chemistry,

check at larger scale

O.Jolliet, 4/13/00

Human Toxicity Potentials (HTP)

Method comparison

Method Pollutant Volume of Residence exposure Hum.Tox.P.

dilution time efficiency HTP

[m3/m2] [day] [-] [kgPb a /kg i

]

Critical Surface-Time

CST air Pba 3500 11 6.8E-7 1

CST soil-food Pb s-f 0.2 200 to 2000 yr 0.024 to 0.24 2000 to 20000

CST air-soil-food Pba-s-f (11% depos.) 0.0028 to 0.028 230 to 2300

CST 95 NOxa 890 1.1 2.5E-7 0.002

CML96/USES "original" NOxa 1000 180 4.0E-5

CML96/USES revised NOxa 1000 1 2.2E-7

O.Jolliet, 3/11/99

C

M M 

Critical surface-time

 (Jolliet and Crettaz, 1994 and 1997):

Possible shortcut:

F i = = δ i

δ i

Full fate: empirical approach

δ C i : measured

δ i : estimated

At regional, continental or world level

--> overall response of the environment

τ i

Vi

O.Jolliet, 3/11/99

Modelling atmospheric fate: atmospheric fate:

a modular approach to fate modellin a modular approach to fate modelling

Vapour in air

Sorption to

aerosol particles

K XA=10 (0.55log (KOW RT/H)-8.23) ρ p

Scavenging of particles 

into precipitation

(W X = 2 105)

Vapour scavenging

into precipitation

K W A = RT/H 

Soil or water

Sedim entation 

(dry deposition)

vdry = 0.15 cm/s 

W et deposition

Vapour partitioning

with surface 

receptors (plants)

vdep = ?

Hydrolysis

Photolysis

vh = khC

Photochem ical

oxidation

v O H = k O H C Transformation

Direct partitioning

Deposition

Sorbed fraction

X = K X A /(KX A + 4 1010)

Gaseous fraction

A = 1- X
51

O.Jolliet, 3/11/99

Intermittent Intermittent wet deposition wet deposition:

variations of concentration in air variations of concentration in air

Concentration

kg/m3

time

days

tdry twet

τdry

Cdry equ

Cdry max

Cwet equ

O.Jolliet, 3/11/99

Residence times: Residence times:

check with measured values check with measured values

predicted residence time of pollutant in air as a 

function of measured residence time

0.1

1

10

100

1000

10000

100000

0.01 1.00 100.00 10000.00

tmeas

O.Jolliet, 3/11/99

Comparison Comparison and check of models and check of models to

include fate include fate and exposure in LCIA

Dr O.Jolliet, P. Crettaz P.Hofstetter E. Hertwich R.Spriensma

EPFL-Swiss Federal ETHZ-Swiss Federal University of Pré consultant

Institute of Technology Institute of Technology California Amersfoort (NL)

Lausanne (CH) Zürich (CH) UC-Berkeley (USA)

SETAC-Europe conference in Bordeaux, May 98

Comparison between CALTOX and EUSES

- Residence time in air and water

- Transfer factor between air and water

- Overall human exposure efficiency

O.Jolliet, 3/11/99

Water

Sediment

Surface soil

Air

Root-zone soil

Roots

Leaves

Deep soil

Gases

Particles

Fugacity fate and exposure model

CALTOX (UC Berkeley): risk assessment

> 150 parameters,

Based on partition coefficient at equilibrium,

O.Jolliet, 4/13/00

Exposure efficiency for air emissions

Models comparison UC-Berkeley / NL

1E-10

0.000000001

0.00000001

0.0000001

0.000001

0.00001

0.0001

0.001

0.01

0.1

1

10

0.0000001 0.000001 0.00001 0.0001 0.001 0.01 0.1 1 10

CALTOX exposure efficiency (-)

EUSES exposure efficiency (-)

UC Berkeley (USA)

On the basis of the EPFL intermediary model

NL Pb air-soil-food

Pb inhalation

Errors

Via food

♦ Inhalation

O.Jolliet, 4/13/00

EUSES (CALTOX) EUSES (CALTOX)

residence half-lives in air

0.000001

0.00001

0.0001

0.001

0.01

0.1

1

10

100

1000

10000

0.00001 0.0001 0.001 0.01 0.1 1 10 100 1000 10000

CALTOX Residence half life in air (d)

EUSES Residence half life in air (d)

Cd Heavy metals Carbon

tetrachloride: d-1

d d-1 -1

Phenol(default)
52

O.Jolliet, 3/11/99

E : effect factor F : fate factor

Ecotoxicity characterisation Ecotoxicity characterisation

(SETAC working group on toxicity assessment toxicity assessment:1996)

Si = Ei Fi Mi

links emission-effect

neglected until 1994 !

Toxicity measure

Si = δCi A ∆t = Mi RfCi

1

RFCi , [kg/pers-an]

Reference concentration δCi , [-] “integrated

concentration increase”

Eco

toxicity: NECi

τ/V

O.Jolliet, 3/11/99

Human toxicity Human toxicity:

effect modelling effect modelling

O.Jolliet, 3/11/99

 F : fate factor

fate information

 E : effect factor

effect information

Toxicity Toxicity characterisation

(SETAC working group on toxicity assessment toxicity assessment:1996)

“background” information

Modifiers

geographical Information

Main

Information

Toxicity score: Si = Ei Fi Mi

O.Jolliet, 3/11/99

Spatial differentiation

In case of large variations:

F and E can be spatially differentiated as a

function of geographical or background

information (water volume, indoor, etc.)

Spadaro, 1998: exsposure indices

Population density: 0.5 rural 1 avg 6 urban

Stack height: 15 (0m) 1 (10m) 0.6 (200m)

For a fully site-specific approach: EIA !

O.Jolliet, 3/11/99

Conclusions

- Variations on exposure efficiency/fate are very

large, up to 106 ( 6 orders of magnitude )

- Consistency between effect and fate is a must !

The choice of the level of the effect factor

depends on uncertainties.

- Exposure efficiency and overall residence times

are useful concepts as an intemediary between

modelling and scoring

- Reasonable consistency between results of

different models, once errors are identified.

Models comparison needs to be performed

systematic., with checks against measurements
53

1. Model development

In the LCA impact assessment, toxicity potentials are

used for a weighted aggregation of the potential impacts of

a large number of toxic pollutants into one toxicity score.

Up to now, toxicity potentials were calculated with multimedia fate models, using unrealistic system settings to meet

LCA requirements. Some of these unrealistic system settings can be overcome by modelling a larger part of the

world, i.e. by using the nested multi-media fate and exposure model USES 2.0. This model has four scales, which

are a local, a regional, a continental and a global scale (Figure 1). The latter consists of three parts, reflecting arctic,

moderate and tropical geographical zones of the Northern

Hemisphere.

Using this model, toxicity potentials were calculated for

the impact categories human toxicity, aquatic ecotoxicity,

terrestrial ecotoxicity and sediment ecotoxicity after initial

emission to the compartments air, fresh water, seawater,

industrial soil and agricultural soil, respectively. Because

the model was originally developed for risk assessment

Priority Assessment of Toxic Substances in LCA

- A Probabilistic Approach -

M. Huijbregts1 and L. Reijnders

Interfaculty Dept. of Environmental Sciences

University of Amsterdam

Amsterdam, The Netherlands

 U. Thissen and A. Ragas

Dept. of Environmental Studies

University of Nijmegen

Nijmegen, The Netherlands

 T. Jager and D. van de Meent

National Institute of Public Health and the Environment,

Bilthoven, The Netherlands

1Address correspondence to: Drs. M.A.J. Huijbregts, Interfaculty Department of Environmental Science, Faculty of Environmental Science, University of Amsterdam, Nieuwe

Prinsengracht 130, NL-1018 VZ, Amsterdam, The Netherlands, tel. ++31 525 62 63, e-mail: m.huijbregts@frw.uva.nl

Figure 1: Schematic representation of the scales in USES 2.0.

MODERATE ZONE TROPIC ZONE

GLOBAL SCALE

CONTINENTAL SCALE

REGIONAL SCALE
54

purposes, a number of LCA-specific model changes were

implemented in USES 2.0. The most important model

changes are:

(1) Discarding the local and regional scales. In current

life-cycle inventories, emissions are summed up per pollutant type regardless of the spatial context of these emissions. This results in an inventory outcome lacking any retrievable relation with a particular region. As a consequence,

the local and regional scales in USES 2.0 are inappropriate

for the computation of toxicity potentials;

(2) Excluding ‘economic’ processes. In LCA there is a

strict distinction between economic processes, which should

be taken into account in the inventory analysis, and the

impact assessment in which the potential impact of these

emissions is estimated. Therefore, economic processes in

USES 2.0, such as waste water treatment and emission

estimates, are switched off in the model calculations;

(3) Standard emissions at the continental scale. The builtin emission estimation procedures of USES 2.0 were not

used, but standard emissions to the different environmental compartments (air, fresh water, seawater, industrial soil

and agricultural soil, respectively) were implemented at the

continental scale. When using continental standard emissions in the computation of toxicity potentials, it is implicitly assumed that the accumulated emissions related to the

complete life-cycle of a product system, i.e. the outcome of

a life-cycle inventory, (i) all take place in Western Europe

and (ii) are uniformly distributed over the initial emission

compartment;

(4) Aggregation of risk characterization ratios (RCRs) at

the continental and global scales. RCRs, also known as

PEC/NEC-ratios, are calculated by dividing the estimated

exposure level with a no-effect parameter for that particular

substance. RCRs form the basis of the calculation of toxicity potentials. USES 2.0 gives the possibility to compute

RCRs on a local and regional scale. However, these scales

are not taken into account in the computation of toxicity

potentials. Consequently, RCRs are computed on the continental scale, and the three global scales for every impact

category. But one is still left with the problem that several

RCRs are computed for the same impact category. Computation of one toxicity potential per initial emission compartment for the four impact categories can only take place

after aggregating the RCRs of the different environmental

scales. This aggregation was performed with weighting factors resulting in a weighted RCR for each toxicity category.

The RCRs of the aquatic compartments were aggregated

on the basis of the compartment’s volume, and the RCRs

of the sediment and terrestrial compartments were both

aggregated on the basis of the compartment’s mass. For

humans, the human population present at a certain scale

has been used as a weighting factor. Thus, per impact category the weighted RCR of a substance after a standard

emission to a certain compartment is calculated by

(1)

in which W is the compartment-specific weighting potential;

(5) Introduction of a reference substance and reference

compartments. It is chosen to use a reference substance

in the calculation of toxicity potentials which follows the

established use of carbon dioxide (CO2), ethylene (C2H4),

and chlorofluorocarbon (CFC11) for evaluating global warming, photochemical ozone formation and stratospheric ozone

depletion, respectively. 1,4-Dichlorobenzene (1,4-DCB) is

taken as a reference substance in the calculation of toxicity

potentials. Furthermore, a reference emission compartment

per toxic impact category is chosen: the air compartment

for human toxicity, the fresh water compartment for aquatic

ecotoxicity and sediment ecotoxicity, and the industrial soil

compartment for terrestrial ecotoxicity. Toxicity potentials

(TP) are calculated by dividing the aggregated RCRs of a

substance after emission to a certain compartment with

the aggregated RCRs of the reference substance after emission to the reference compartment:

(2)

resulting in 20 toxicity potentials per substance (see Table

1).

2. Probabilistic Approach

Apart from the possibility to calculate toxicity potentials

in a deterministic way, it is also possible to follow a probabilistic approach in which the operational uncertainty in the

toxicity potentials is quantified by specifying the uncertainties in the input parameters and performing Latin Hypercube

sampling. To save time and money in collecting data for

Weighted RCR (PEC x W)

NEC

= Σ

TPx WeightedRCRx

Weighted RCR ref =

Table 1. Type of Toxicity Potentials per Substance

Emission compartment Fresh Sea Industrial Agricultural

Impact category Air Water Water Soil Soil

Aquatic ecosystems AETPair AETPfw AETPsea AETPis AETPas

Terrestrial ecosystem TETPair TETPfw TETPsea TETPis TETPas

Sediment ecosystem SETPair SETPfw SETPsea SETPis SETPas

Humans HTPair HTPfw HTPsea HTPis HTPas
55

defining density distributions, only the most important parameters may be considered in detail. Those parameters

can be identified by a preliminary uncertainty importance

analysis. In the preliminary analysis, the uncertainty distributions of all parameters are estimated only roughly, with

the aim to determine what input parameters contribute most

to the uncertainty of the output parameters. The parameters

that appeared to contribute insignificantly to the output uncertainty can be replaced by their most likely values. From

our preliminary analysis, it appeared that in general uncertainty in substance-specific input parameters from the reference substance and environmental characteristics will not

contribute significantly to the uncertainty of LCA toxicity

potentials. Consequently, only substance-specific input parameters of the substances under study and human characteristics should be specified in more detail. This can be

done in a second more detailed uncertainty analysis, resulting in frequency plots of the output parameters representing the operational uncertainty of the toxicity potentials.

To simplify the implementation of the uncertain toxicity

potentials in LCA case studies, the sampled distributions

may be fitted to standard uncertainty distributions, such as

Lognormal and Gamma distributions. The uncertainty in the

toxicity potentials may be represented by an uncertainty

factor k which is defined such that 95% of the values of a

stochastic variable (X) is within a factor k from the median

M(X) of a skewed distribution:

(3)

As these uncertainty distributions are to be used in LCA

case studies, correlations between the toxicity potentials

should also be known. The toxicity potentials of one substance may be correlated, because they all stem from the

same uncertain substance-specific parameters.

p

M(X)

k

> <X k x M(X) 0.95 F

H

G I

K

J =

3. Discussion

Although both method and input data were improved in

comparison to former research, still a number of further

improvements should be addressed. First, model improvements in USES 2.0 itself are needed. For instance, the

groundwater compartment is not implemented in the model

as a full compartment. LCA-specific changes in the model

structure may also have an effect on the validity of the model

results. One item for improvement relates to the geographical choice. An important assumption in the calculation of

toxicity potentials is that all life cycle emissions take place

in Western Europe. Of course, this is a simplification of

reality because emissions in many product life cycles take

place partly outside Western Europe. Another problem of

using Western Europe as the initial emission compartment

is that, in this way, all emissions are assumed to be homogeneously distributed over Western Europe, not allowing

computation of specific toxicity potentials per West European country. Finally, the aggregation of RCRs related to

aquatic and terrestrial toxicity take place on the basis of

volumes and weights of the related compartments, respectively. Other weighting methods, for instance, on the basis

of species density per compartment may result in other

toxicity potentials for aquatic, terrestrial and sediment ecosystems.

Huijbregts’ Biography

Huijbregts studied Environmental Science and Health

Science at the University of Nijmegen from 1990 to 1996. In

1996, he started his Ph.D. research at the Interfaculty Department of Environmental Sciences from the University of

Amsterdam. The main goal of this research is to perform an

uncertainty analysis for the Life Cycle Assessment of lowenergy options applied in Dutch one-family buildings. Recent publications are: Huijbregts, M.A.J., 1998. Application

of uncertainty and variability in LCA. Part I: A general framework for the analysis of uncertainty and variability in life

cycle assessment. Int. J. LCA 3 (5): 273-280, and Part II:

Dealing with parameter uncertainty and uncertainty due to

choices in life cycle assessments. Int. J. LCA 3 (6): 343-

351
56

Huijbregts’ Overheads

Priority Assessment of Toxic Substances in LCA

A Probabilistic Approach

M. Huijbregts & L. Reijnders (IDES- UvA)

U. Thissen & A. Ragas (Dep. Env. Studies, Nijmegen)

T. Jager & D. van de Meent (RIVM-ECO)

Brussels, 30 November 1998

1. Introduction

Why Toxicity Potentials in LCA?

• Weighted summation of toxic emissions

• Toxic Impact categories

(1) Humans

(2) Aquatic ecosystems

(3) Terrestrial ecosystems

How are current Toxicity Potentials calculated?

• Fate & Effects are taken into account

(Guinée et al., 1996; Hertwich et al., 1998)

• Use of Reference Substance

Improvements needed in current approach?

• Avoiding the artificial change of system parameters

• Taking into account all relevant compartments

• Usage of realistic values instead of defaults

Goal

• Improvement of model structure

• Show effect of data uncertainty and variability

2. Methods

Uniform System for the Evaluation of Substances

2.0

• Global nested multi-media fate model (Simplebox 2.0)

• Human exposure model

• Risk Characterisation Ratios
57

Adaptations needed for LCA purposes

• Regional and local scale are turned off

• Economic processes are turned off

• Standard emission at continental scale

• Weighted summation of RCRs per impact category

• Use of reference substance and compartment

Calculation of Weighted Risk Characterisation Ratios

Emission of substance on

 West-European scale

Aquatic PEC/PNEC

• Western Europe

- fresh water

- seawater

• Moderate zone

• Arctic zone

• Tropic zone

Terrestrial PEC/PNEC

• Western Europe

- Natural soil

- Agricultural soil

- Industrial soil

• Moderate zone

• Arctic zone

• Tropic zone

Human PDI/HLV

• Western Europe

• Moderate zone

• Arctic zone

• Tropic zone

Weighted

Aquatic RCR

Weighted

Human RCR

Weighted

Terrestrial RCR

Aquatic

 Weighting Factors

Human

 Weighting Factors

Terrestrial

Weighting Factors

Probabilistic simulation

• Sampling method: Latin Hypercube

- Substance-specific input data

- Human characteristics

- No system parameters

• Importance analysis: Spearman rank correlation

• Covariance matrix: Spearman rank correlation

• Separate treatment of Reference Substance

Weighted Aquatic RCR after emission to fresh water

2,3,7,8-TCDD

.000

.057

.114

.171

.229

0

285

571

857

1143

0.0E+0 6.3E-3 1.3E-2 1.9E-2 2.5E-2

Probability Frequency

Median = 1E-3 Mean = 2.4E-2 k = 2.3E+3

1,4-Dichlorobenzene

.000

.029

.059

.088

.118

0

147

294

441

588

0.0E+0 2.3E-7 4.5E-7 6.8E-7 9.0E-7

Probability Frequency

Median = 4E-8 Mean = 1.1E-7 k = 4.9E+1

Weighted Aquatic RCR after emission to fresh water

Weighted Terrestrial RCR after emission to indust soil

2,3,7,8-TCDD

.000

.008

.017

.025

.033

0

42

83

125

167

0.0E+0 1.3E+1 2.5E+1 3.8E+1 5.0E+1

Probability Frequency

Median = 7.0 Mean = 13.7 k = 1.4E+1
58

1,4-Dichlorobenzene

.000

.036

.072

.108

.143

0

179

358

537

717

0.0E+0 3.8E-3 7.5E-3 1.2E-2 1.5E-2

Probability Frequency

Median = 1.0E-4 Mean = 2.7E-2 k = 1.7E+6

Weighted Terrestrial RCR after emission to indust soil Weighted Human RCR after emission to air

2,3,7,8-TCDD

.000

.043

.085

.128

.171

0

213

427

640

854

0.0E+0 6.3E+6 1.3E+7 1.9E+7 2.5E+7

Probability Frequency

Median = 1.3E+6 Mean = 1.2E+7 k = 1.3E+3

1.4-Dichlorobenzene

.000

.034

.069

.103

.138

0

172

344

516

688

0.0E+0 2.5E-5 5.0E-5 7.5E-5 1.0E-4

Probability Frequency

Median = 7.8E-6 Mean = 1.5E-4 k = 7.8E+3

Weighted Human RCR after emission to air Uncertainty importance

Aquatic ecotoxicity

Fresh water emission

Terrestrial ecotoxicity

Industrial soil emission

Human toxicity

Air emission

Parameter Importance Parameter Importance Parameter Importance

Koc 63 % Kdeg_soil 75 % AF_inter 72%

AFlab-field 24 % AFlab-field 24 % BAF_meat 12 %

Kdeg_sed 6 % Kdeg_soil 5 %

Kdeg_water 3 % VP 4 %

VP 3 % AF_intra 3%

TOTAL: 99 % TOTAL: 99 % TOTAL: 96 %

Covariance matrix

AIR FW SEA AGR

A T H A T H A T H A TH

A 1

AIR T 1

H 1

A 1.0 1

FW T 0.7 1

H 0.9 1

SEA A 1.0 1.0 1

T 0.7 1.0 1

H 0.9 1.0 1

A 0.9 0.9 0.8 0.9 1

AGR T 1.0 1

H 0.9 0.8 0.8 1

A 0.9 0.9 0.8 0.9 1.0

IND T 1.0 1.0

H 0.8 0.9 0.9 0.8

Discussion & Conclusions

Validity of results

• Model structure is more in line with LCA

• Uncertainties in model structure are still important

• Standard emission at West European scale is not fully

in line with LCA

• Simple weighting factors are used within impact

categories

• Defining input uncertainties is uncertain itself
59

Significance of results

• Human variability and system parameter uncertainty

are not of significant importance

• Only a few substance-specific parameters are

responsible for uncertainty in Toxicity Potentials

• Correlations between Toxicity Potentials are large for

the same substance and impact category

• Care must be taken with implementation of Toxicity

Potential uncertainties in LCA case studies

- Reference substance

- Correlations

- Overlap in product comparisons
60

Site-specificity in characterization within Life Cycle Impact Assessment (LCIA) is a highly active area of current

research and development. This level of activity may largely

be in response to two factors: (1) past criticisms of the

simplicity of generic or “global” methods for characterization analysis, and (2) empirical information which indicates

that location can contribute to multiple orders of magnitude

differences among appropriate characterization factors, for

emissions emanating from a given site.

However, the emissions within an LCA inventory emanate from many sites, not one. The important question is

how many. If only a few sites contribute the bulk of emissions for most products’ life cycles, then site-specificity is

essential and feasible. If on the other hand, scores of sites

contribute significantly to inventory emissions totals for most

products, then site-specificity is less feasible and much

less necessary. Thus, the need for location-based characterization factors, and the uncertainty reduction benefits

which result from applying locational information in LCIA,

depend critically upon an unexamined issue: the structure

of the emissions inventory.

By “structure” we mean a few related attributes of the

inventory emissions. First, of importance is the minimum

number of sites whose emissions must be taken into account in order for an analysis (such as an LCIA) to address

high percentages (e.g., 80% or 90% or 95%) of the total life

cycle emissions. More generally, the shape of the ranked

cumulative distribution of emissions is important and of interest.

The above factors influence the spatial distribution of the

emissions, which, together with the regional variability in

appropriate characterization factors, ultimately determines

the uncertainty reductions which accrue from site- or region-specific LCIA. How geographically dispersed are the

life cycle emissions? On the one hand, we want to be able

to address this question for specific life cycle inventories.

At the same time, it would be highly valuable to gain some

insight about the answers to these questions about inventory structure for life cycle inventories in general, to help

guide the development of LCIA in general.

This presentation summarized results of empirical investigations into the issues outlined above. First, a method

and database were presented for addressing the dependence

of terrestrial (non-oceanic) deposition of sulfur and nitrogen

upon the geographic location of SO2 and NOx emissions

sources. A modified characterization factor that accounts

for predicted terrestrial deposition was introduced. Next, the

influence of the number of emissions sites in an inventory

upon the expected inventory characterization error was simulated and presented, under the simplifying assumption of

equal emissions from all inventory sites, and random geographic distribution of sites. Following this discussion, an

input/output LCA system for the USA was briefly described,

as a means for characterizing aspects of the inventory structure in general, across products. Finally, the presentation

summarized the implications of the concepts for desired/

required levels of specificity in life cycle impact assessment, and for uncertainty analysis and decision making in

LCA.

Norris’ Biography

Norris has been active in Life Cycle Assessment for 5

years. He founded Sylvatica, an LCA-related consultancy

located in Maine, USA. He conducts research in LCA and

Industrial Ecology-related topics for several US Federal

Agencies, and applies LCA on behalf of private corporations and non-profit research institutes. Norris is adjunct

professor with the Complex Systems Research Center at

the University of New Hampshire, and director of US Operations for the international Athena Institute, which develops LCA databases and decision support software for building designers and architects. He has degrees in Engineering and Natural Resources, from MIT, Purdue University,

and the University of New Hampshire.

Implications of Inventory Structure for

Life Cycle Impact Assessment and Uncertainty Analysis

Gregory A. Norris

Sylvatica, Maine, USA.
61

Norris’ Overheads

Specificity in LCIA: Specificity in LCIA:

Towards a Value-of-Information Approach

Gregory A. Norris

Sylvatica / Univ. of New Hampshire / Harvard Univ.

USA

Our Conference Organizers are to be

Thanked and Congratulated for Posing

the Question: the Question:

“What levels of sophistication are

needed, desirable, and useful needed, desirable, and useful 

in Life Cycle Impact Assessment?”

To answer this question: this question:

■ Requires a quantitative assessment of the

uncertainty reduction payoff uncertainty reduction payoff in results

achieved by increasing sophistication

➨ requires LCA with uncertaint requires LCA with uncertainty analysis as a

function of modeling/methods choices

➨ what matters is what matters is clarity of decision support clarity of decision support as a

function of choices -- reliabilit function of choices -- reliability or confidence in

conclusion that conclusion that “A < B”

■ If done well will also point to places in

analysis where uncertainty reductions yield

biggest gains in clarity of decision support ains in clarity of decision support

A main message A main message

• Most guidance to our search for Most guidance to our search for

appropriate levels of sophistication must appropriate levels of sophistication must

come from context of come from context of full analysis full analysis

• Can’t make much headway when LCIA

is isolated from LCI and Valuation

LCI LCIA

Char. Valuation Decision

“you’ve got to look at the whole system...”

■ not this claim again! not this claim again!

■ “Prove it. Prove it.”

■ “And Be Specific. And Be Specific.”

■ “With real examples, real data. With real examples, real data.”

Specifically... Specifically...

FOR ONE THING: FOR ONE THING:

■ Clearly, site-specific factors may dramatically

increase precision when characterizing sitespecific emissions. specific emissions.

■ But do they matter for the LCIA results as a

whole?

■ Will they increase the clarity of decision

support?

■ Will they appreciably narrow confidence

intervals on [A-B]?
62

Specifically... Specifically...

Suggest that Suggest that at least at least these factors matter these factors matter

when searching for the desired level of soph.:

1 The structure of the inventory system

2 Uncertainty in the LCI results

3 The appropriate modeling, and our ability to

model, regionality of inventory emissions

4 Decisions about valuation

■ Examples drawn from: Examples drawn from: Regionalized CFs

taking account of taking account of SOx and NOx transport, transport,

deposition, and concentration effects in USA

Example Startin Example Starting Point

■ Tracking and Assessment Framework Tracking and Assessment Framework

■ US DOE Integrated Assessment, 1996

■ Valuation of impacts of reducing sulfur Valuation of impacts of reducing sulfur

and nitrogen pollution emissions and nitrogen pollution emissions

■ Human respiratory, visibility, and limited

ecological effects (aquatic toxicity in a

few example lakes --> recreation) few example lakes --> recreation)

Source/Receptor Matrices Source/Receptor Matrices

■ Basis: Lagrangian ASTRAP model, validated

against against Eulerian RADM model RADM model

■ Translates: seasonal emissions of Translates: seasonal emissions of NOx and

SO2 per US (source) state (KT/season) SO2 per US (source) state (KT/season)

■ Into:

■ Seasonal wet & dry dep/ (KT S, N per (receiving)

US state, 10 Canadian prov., northern Mexico; ., northern Mexico;

■ Delta avg. seasonal ambient concentrations of . seasonal ambient concentrations of

nitrogen and sulfur species (mg/m3)

■ Also characterizes climate variability

(...to model of uncertainty* resulting from

using generic characterization factors

when emissions occur at when emissions occur at

randomly selected (i.e., unknown) sites)

* Expected characterization prediction error

The Multiplicity Question(s) The Multiplicity Question(s)

■ How many sites, How many sites, 

with how much with how much geographic dispersion, raphic dispersion,

contribute si contribute significantly to inventory totals?

■ What are the expected shapes of these

distributions?

■ We would like to know these answers We would like to know these answers

for LCI’s in general -- that is, in general -- that is, 

for products for products’ inventories in general. inventories in general.

The Multiplicity Question(s) The Multiplicity Question(s)

■ CHALLENGE 1: CHALLENGE 1: 

The results will be strongly influenced by

the boundaries of the LCI -- need very

comprehensive LCIs.

■ CHALLENGE 2: CHALLENGE 2: 

There are a lot of products. There are a lot of products.
63

US Input/Output-Based Upstream LCI: US Input/Output-Based Upstream LCI:

500 sectors consuming goods and services from other oods and services from other

sectors, emitting pollution, and consuming resources

US Input/Output-Based Upstream LCI: US Input/Output-Based Upstream LCI:

■ Consumed inputs are measured in $: Consumed inputs are measured in $:

include materials, fuels, intermediate

products, products, equipment, services equipment, services

■ Allocation among products on $ basis Allocation among products on $ basis

■ Pollution emissions in kg/$ of output Pollution emissions in kg/$ of output

■ Input/output data result from census Input/output data result from census

■ Annual sectoral sectoral emissions inventories

Elements of the Elements of the 

Input/Output View of Multiplicity Input/Output View of Multiplicity

✔ No boundary truncation

➶ Multiple product outputs, modeled as 1

➶ not all inputs may be used for be used for given product iven product

➶ many inputs used for more than one product inputs used for more than one product

➴ Each input flow modeled as 1 flow

➴ net result: likely to be undercounting sites

➴ Model each input as single-supplier Model each input as single-supplier

● Can re-do, Can re-do, supressing inputs of services, etc. inputs of services, etc.

(to model of (to model of tierwise emissions) emissions)

What does the distribution of What does the distribution of

emissions within each tier look like?

(to model of site-by-site pollution

within each tier -- within each tier --

next time!) next time!)

Two final, very brief, topics Two final, very brief, topics

■ Correlation and making distinctions Correlation and making distinctions

■ A “Value Space” heuristic heuristic
64

Correlation, Canceling, and Correlation, Canceling, and

Confidence about Differences Confidence about Differences

Cannot draw conclusions by comparing

PDFs or CDFs! Below could imply... ! Below could imply...

B A

B A

...either of the following: either of the following:

■ Must Simulate the Difference! Must Simulate the Difference!

■ Requires simulated model to contain

information on correlations and/or their and/or their

structural causes. structural causes.

0 A-B 0 A-B

Value of Information Analysis Value of Information Analysis

■ How might we apply uncertainty in LCA

decision making

■ How do we treat valuation and

uncertainty jointly for decision support uncertainty jointly for decision support

■ Which uncertainties matter most Which uncertainties matter most

■ What level of sophistication is needed

The “Value Space” as a heuristic for VOI as a heuristic for VOI

and distinguishing uncertainties from

decisions/values

V2 / V1

V3 / V1

0.1

1

10

0.1 1 10

95% CI’s for S’s lower bound

0 2

5

The “Value Space” as a heuristic for VOI as a heuristic for VOI

and distinguishing uncertainties from

decisions/values decisions/values

V2 / V1

V3 / V1

0.1

1

10

0.1 1 10

Confidence isoclines for S>0

80% 90%

95%

Example with higher uncertainty

V2 / V1

V3 / V1

0.1

1

10

0.1 1 10

95% CI’s for S’s lower bound

-2 0

2
65

Example with higher uncertainty

V2 / V1

V3 / V1

0.1

1

10

0.1 1 10

“Confidence isoclines” for S>0

50% 80%

90%

Example with higher uncertainty Example with higher uncertainty

V2 / V1

V3 / V1

0.1

1

10

0.1 1 10

“Confidence isoclines” for S<0

50% 10%
66

Introduction

The lack of spatial differentiation in current life cycle impact assessment affects the relevance of the assessed

impact. A framework has been developed for how to arrive

at factors that relate the region of emission to the acidifying

impact on its deposition areas. Next, such factors are established for 44 European regions with help of the RAINS

model.

RAINS is an integrated assessment model that combines

information on regional emission levels with information on

long range atmospheric transport in order to estimate patterns of deposition for comparison with critical loads1

 for

acidification2.

Application of the acidification factors in life cycle impact

assessment is straightforward. The only additional data required, the geographical site or region where an emission

takes place, is in general already provided by current life

cycle inventory analysis, or by the scoping of the product

system. The use of the acidification factors add resolving

power of a factor thousand difference between highest and

lowest rating, while the combined uncertainties in RAINS

are cancelled out to a large extent in the acidification factors due to the large area of ecosystems they cover. The

information gained by the use of these factors thus compensates fully for the additional uncertainties they introduce.

The framework presented is suitable to establish similar

factors for eutrophication and tropospheric ozone creation.

RAINS also covers these impact categories.

An extensive description of the developed framework, and

the achieved acidification factors are given in Potting et al.

(1998a,b). Since these publications cover also quite well the

presentation at the workshop, the presentation is not further summarized here. Rather, some main discussion points

in relation to site-characterization are raised and commented

upon here.

Different Levels of Sophistication

Due to lack of time, it was impossible to elaborate in the

presentation on the different levels of sophistication allowed

by the acidification factors of Potting et al. (1998a,b). Since

these publications do not discuss this issue either, it is

addressed in more detail here.

The level of sophistication in impact assessment can, as

mentioned a few times during the workshop, be understood

in two ways (see also Potting et al. 1997b

):

1. The extent to which relevant parameters in the causality chain are taken into account in the characterization

factors (i.e. whether the characterization factors are

based on no, some or full fate and exposure modeling).

2. The extent to which spatial (and temporal) variation is

allowed in each parameter of the modeling underlying

the characterization factors

The acidification factors from Potting et al. (1998a,b) are

sophisticated in both senses. They cover all the relevant

Levels of Sophistication in

Life Cycle Impact Assessment of Acidification

José Potting and Michael Hauschild

Institute for Product Development,

Building 424, Technical University of Denmark

DK-2800 Lyngby

Denmark

Telephone: +45-45-254677

Fax: +45-45-935556

Email: JP@IPT.DTU.DK

1As a matter of fact, RAINS works with critical load functions, giving all combinations of sulphur and nitrogen deposition above which ecosystems are at risk to be

damaged (see Posch et al. 1995). For the sake of clarity, the term “critical load” is

used in this paper in stead of the correct term “critical load function”. 2

RAINS contains projections of the actual emissions for 44 regions covering all of

Europe, including the European part of the form Soviet Union. The model first calculates atmospheric dispersion and depositions from the emissions of all European

over the 612 grid elements (150* 150km) that together form the European grid.

Next, the total deposition in each grid element is compared with the critical loads

for all ecosystems within that grid element (hence that several grid elements distinguish more than several thousand ecosystems; Posch et al. 1995). In this way, the

area of unprotected ecosystem is determined per grid element (i.e., the area of

ecosystems where critical loads are exceeded). Finally, the model accumulates

over all grids resulting in the total area of unprotected ecosystems over the full

European grid. These features of RAINS are used to calculate acidification factors

that quantify the change in area of unprotected ecosystems caused by the change

in the emission of one region (while background emissions and resulting depositions from other regions remain the same). See Potting et al. (1998a,b) for further

clarification.

)
67

parameters in the causality chain, and they allow a high

degree of spatial variation. Next Sections will discuss other

types of acidification factors with a similar level of sophistication. This Section will go into the limitations in spatial

differentiation in the factors from Potting et al. (1998a,b).

As already mentioned, application of the acidification factors from Potting et al. (1998a,b) in life cycle impact assessment is quite straightforward. Each emission is multiplied

with the acidification factor for the relevant substance and

region. Next the product from all emissions times acidification factors are summed-up to arrive at the total acidifying

impact from the analyzed product.

Application of the acidification factors from Potting et al.

(1998a,b) requires data additional to current impact assessment: The geographical site or region where an emission

takes place. The requirement of this additional data is often

put forward as an objection against spatial differentiation.

However, the geographical site or region where an emission

takes place is in general already provided by current life

cycle inventory analysis (since this data is needed to calculate the interventions from transport). Nevertheless, this

spatial differentiation may not always be possible or desired. In that case, the acidification factors from Potting et

al. can be used also to establish a site- or region-generic

default per substance. Such default can for example be

based on the European average acidification factor. Though

these defaults avoid spatial differentiation, they do cover all

relevant parameters in the causality chain (in contrast with

acidification factors based on the potential of substances

to release hydrogen ions). An additional advantage is that

at least the uncertainties posed by refraining from spatial

differentiation are known.

The acidification factors of Potting et al. (1998a,b) do account quite accurately for regional dispersion, but local dispersion is modeled rather poorly in RAINS3

. There are models available that estimate regional dispersion similar as

RAINS, but account in a more sophisticated way for local

dispersion. Better modeling of local dispersion probably

contributes little to the accuracy of the acidification factors,

however, since the first few kilometers only have a small

share in the total impact from long-lived substances as sulfur and nitrogen. In addition, such additional local modeling

would increase considerably the data demands from inventory analyses. Nevertheless some life cycle assessments

do allow (and also desire) such high level of spatial differentiation as discussed in Potting et al. (1999). In those cases,

the more sophisticated modeling of local dispersion can be

used in addition4

.

It would be interesting to investigate what additional information different degrees of spatial differentiation provide,

and for which application they are relevant (see also the

presentation of Norris elsewhere in these proceedings).

Basis for Spatial Characterization

The RAINS model also contains the data to establish

impact factors for regional ozone formation and for terrestrial eutrophication from atmospheric depositions. A similar

framework as for the acidification factors can be followed

for these impact categories. Results will be published in

Potting and Hauschild (forthcoming). These three impact

categories have some general features in common that

actually provide the basis for spatial differentiation.

As we emphasized in Potting et al. (1998a,b), Potting and

Hauschild (1997a,b), and several presentations, many present

environmental problems are characterized by the fact that

the exposure of any receptor is the result of the long distance transport of emissions from very many sources. The

background exposure of one single receptor results thus

from multiple sources (far more than hundred thousand5).

The other way around, one single source contributes to exposure of very many ecosystems (also far more than hundred thousand6

). This means that a single source (thus inherently also an emission per functional unit) contributes

only very little, in most cases marginally to the background

exposure of any receptor. This means also that the time

behavior of the emission from a single source (i.e. whether

it is a flux or a pulse) is hardly important. The temporal

variation of the very small contribution from our single source

emission will namely be cancelled out anyway against the

very high background exposure from all sources together.

These are the characteristics that provide the possibility

to establish site-dependent impact factors that with considerable accuracy estimate the given impact over the whole

European grid posed by a single source located in a given

region. To establish these impact factors, one needs an

assessment model like RAINS that integrates information

on regional emission levels with atmospheric transfer-matrices and threshold evaluation.

Uncertainties in Threshold Information

A hot item at the workshop was the use of threshold information. Finnveden showed in his presentation (see else3The dispersion estimates in RAINS are based on trajectory modelling. A trajectory

model assumes emissions to disperse homogeneously within an air parcel and next

follows this air parcel on its way of atmosphere motion. While the assumption of

homogenous dispersion not affects the accuracy in the medium to long range, it

does lead to an underestimate of concentrations within the first few kilometres from

a source. That is because the course of the concentrations from an emission will

rapidly increase and decrease again within these first kilometres, after which it

continues to decrease more gradually. More accuracy in the first kilometres can be

obtained by Gaussian plume modelling. There are models available that integrate

Gaussian plume modelling with trajectory modelling.

4Hence that critical loads are available on rather detailed scale (see previous footnote), which facilitates evaluation of deposition patterns with high spatial resolution.

5This follows logically from the breakdown of total deposition in one grid element

into the separate contributions from each region (RAINS is able to provide this

information). The emission of each region (usually similar with countries) is on its

turn the total from all small and big sources individually in that region.

6

Relative long-lived substances as sulphur and nitrogen distribute over several

thousand kilometres. Depositions from a particular source occur thus basically

over the whole European grid. The number of ecosystems distinguished over the

full European grid amounts to almost 700,000 in Posch et al. (1997).
68

where in these proceedings) that uncertainty in threshold

information could be:

• A factor two due to parameter uncertainty (see his example about critical loads for acidification), and

• Up to a factor million due to differences in the chosen

endpoint and/or way of effect modeling (see his example

about no-effect-levels for human toxicity).

These large uncertainties raised during the workshop the

justified question whether characterization factors in life

cycle assessment should be based on threshold information. We like to add to a few additional elements to this

discussion.

The impact assessment phase emerged from the wish to

aggregate the large amount of data from inventory analysis

to a manageable amount of impact data. For most impact

categories, initially rather simple modeling was used to establish characterization factors. These characterization factors were limited to equivalency assessment on the basis

of intrinsic substance characteristics like the potential to

release hydrogen ions (acidification assessment) or (no-)

effect-levels (toxicity assessment)7

. No assessment of

threshold exceedance (i.e. PEC/PNEC ≥1)8 was performed

since the available data did not allow such evaluation. Threshold information, usually in the form of a no-effect-level, was

used in toxicity assessment only to express the emission

of a given substance in the equivalent emission of a reference substance, or as a dilution volume of the receiving

environment. The basis of equivalency was taken in the

toxicity potential of each substance9. The impact from an

emission quantity equal to the no-effect-level was put on

one, and the impact from any deviating quantity as the ratio

of the emission quantity divided by the no-effect-level. This

provided the possibility to aggregate substances with different toxic effects9

.

Present toxicity factors cover now also fate and exposure modeling, but aggregation of the calculated exposure

increases from different substances is still often based on

no-effect-levels10 ( ∆ PEC/PNEC; see elsewhere in these

proceedings the presentation of Hertwich, Jolliet and

Huijbregts).

Threshold Information in Life Cycle

Assessment

Though evaluation of threshold exceedance was initially

not performed due to lack of data, it has meanwhile turned

for many practitioners into a principle in itself that is justified by the reasoning that “less pollution is better”. The threshold discussion is topical for already quite some years now.

Unfortunately, the discussion seems to get stuck in a controversy about “less is better” versus “only above threshold”.

As mentioned in the previous section, the question was

raised at the workshop whether characterization factors in

life cycle assessment should make use of threshold information because of their uncertainty. Reluctance was expressed with regard to acidification and eutrophication in

particular. Refraining from the use of threshold information

in toxicity assessment was on the other hand not a point of

discussion10. This is surprising, since the presentation of

Finnveden illustrated that uncertainties in threshold information about toxicity of substances are far larger than for

acidification and eutrophication.

Present toxicity factors use threshold information to facilitate aggregation of exposure increases of different substances (see also previous section). The use of threshold

information inherently requires making a threshold evaluation. As the next section will show, this applies as well for

“less is better” methods!

We like to stress here that the uncertainties in toxic noeffect-levels are as a matter of fact far more problematic

than the uncertainties in the threshold information of other

impact categories11. On the other hand, the modeling of fate

and exposure are sources for considerable uncertainty as

well in toxicity factors particularly. It would thus not solve

any problem to exclude threshold information as basis for

characterization factors in life cycle assessment.

We fully agree with Finnveden that the large uncertainties

in threshold information (together with other uncertainties)

are of major concern. However, it seems far more relevant

to discuss how to deal with those uncertainties, than simply to abandon the use of uncertain information from life

cycle assessment. Such discussion does also justice to

the valuable points brought forward by Finnveden.

As we will discuss in the next sections, the issue of how

to deal with uncertainties in threshold information is quite

another one than the issue of how to perform threshold evaluation.

7

No-effect-levels are based on experiments on test-species under laboratory conditions and therefore say something about the intrinsic potential of a substance to

cause toxic effect (rather than something about the sensitivity of a species in reallife for this toxic substance).

8

PEC is the acronym of Predicted Environmental Concentration. PNEC is the acronym of Predicted No Effect Concentration.

9

The underlying assumption is that the toxicity impact from a quantity at the noeffect-level of one substance has the same importance as the toxicity impact from

a quantity at the no-effect-level of another substance. To put it more clearly: If the

quantities of both substances are at their no-effect-level, the impacts from a neurotoxic substance and an irritating substance are regarded as equally important. The

adding together of completely different effects is one of the more serious problems

in LCA, but not further addressed here.

10Threshold information is still needed to aggregate different substances. If it were

no longer acceptable to base toxicity factors on no-effect-levels because of their

uncertainty, we would almost be back to square one. There would be little possibilities left than to start again adding together toxic substances on mass basis, or to

report them as separate impact categories.

11The critical loads for acidification for instance are based on the capability of

target-systems to cope with system pressure, but not on the effect of the individual

substance as such (as is the case for toxic no-effect-levels). The capability of

target-systems to cope with acidifying substances depends mainly on the characteristics of the exposed target system to neutralize and/or remove acidifying substances and the sensitivity of the target species for acidifying exposure. As a

result, the uncertainties in critical loads are mainly due to uncertainties in target

characteristics, but hardly resulting from uncertainties in substance characteristics. Since critical loads are based on system pressure, also uncertainties posed

by differences in the chosen endpoint and/or way of modelling effect are hardly at

stake (in contrast with toxic no-effect-levels).
69

“Less is Better” Also Performs Threshold

Evaluation

We would like to elaborate a little bit further on the use of

threshold information as basis for characterization factors.

Although up to now implicit, also “less is better” methods

do perform a threshold evaluation by basing their characterization factors on threshold information. We like to illustrate

and clarify with an example about acidification. We will use

this example to illustrate other forms of threshold evaluation as well.

Let’s assume that we have three ecosystems receiving

similar quantities of acidifying substance from our functional

unit12. Let’s further assume that these ecosystems have a

priori equal tolerance for acidifying loading (i.e. similar critical loads), but that they are now at different levels of background loading:

• A Scottish ecosystem with a background load at that

critical load

• A Scandinavian ecosystem with a background load far

below that critical load

• A German ecosystem with a background load far above

that critical load

We want to prioritize for which ecosystem we like to reduce the full acidifying load (background load + marginal

increase from our functional unit).

All ecosystems receive an equal contribution from our

functional unit and have also similar critical loads. “Less is

better” acidification factors consistent with the existing ones

for toxicity can easily be established from the increase in

the acidifying load divided by critical load for the given ecosystems ( ∆ PAL/CL)13. Since all ecosystems have the same

critical load, this would result in a similar acidification factor

for each of the above ecosystem. The background concentration does not play a role here.

The practical implication is that the ecosystem exposed

around the critical load is regarded equally important as the

ecosystem exposed far above the critical load (and thus

difficult to rescue), and as equally important as the ecosystem exposed far below the critical load (and thus hardly in

danger). Whether these ecosystems are exposed far below

or at or far above their critical loads, they are all characterized as being equally vulnerable in such a “less is better”

approach. That might be or might not be a justified choice,

but it is at least important to realize that such choice is

made by following a “less is better” approach!

“Only Around Threshold” An Alternative?

We make another choice than the above “less is better”

approach. We would like to rescue that nice ecosystem in

Scotland that is still unspoiled but about to be damaged.

We therefore would like to prioritize the Scottish ecosystem. We would thus like to reduce the acidifying load on the

Scottish ecosystem rather than the load on that Scandinavian ecosystem or the load on that German ecosystem.

One source (i.e. one process) contributes to very many

ecosystems (far more than hundred thousand). Those ecosystems may differ in their acidifying background loads (as

in the above example), but they can also differ in sensitivity

(i.e. the moors in Scotland, and the rather insensitive ecosystems/soils in South Europe). In Potting et al. (1998a,b),

we based our characterization factors on the slope of the

sigmoid dose-effect curve that is defined by the critical loads

of all ecosystems to which one source contributes. We established the slope of this dose-effect curve in the working

point that is determined by the operative background loads

and thus accounted for both differences in ecosystem sensitivity and differences in background load!

The slope of the sigmoid dose-effect curve used by Potting et al. (1998a,b) is one way to account for both differences in ecosystem sensitivity and differences in background load. The characterization factors based on this slope

tell us what area of ecosystem becomes unprotected as a

result of an emission. The practical implication of using the

change in unprotected ecosystem area is, as we have always emphasized explicitly, that ecosystems with depositions far above or far below their critical loads are not expressed in the characterization factor (it might inspire one

to name “our” approach as “only around threshold”). That

may be or may not be regarded as a disadvantage. However, several other possibilities are available to deal with

both differences in ecosystem sensitivity and differences

in background load.

Other Ways of Threshold Evaluation

In Potting et al. (1998a,b), we propose to transfer “our” riskbased dose-effect curve into a damage-based curve to overcome this problem. A critical load value can tell if there is a

risk of ecosystem damage (i.e. the acidifying load is at or

above critical load), but it doesn’t tell whether this risk actually results in damage or how large this damage will be.

Nevertheless, one expects the damage to become asymptotically larger with increasing exceeding of critical load

values. This can be used to establish acidification factors

in which all ecosystems exposed above ecosystem are reflected, but in which ecosystems exposed around critical

load have higher importance than ecosystems exposed far

above critical load. The damage oriented eco-indicator of

Goedkoop follows basically a quite similar approach, although his acidification factors are based on a different endpoint and lacks for the time being fate and exposure modeling (see elsewhere in these proceedings).

Lindfors et al. (1998) established acidification factors

based on models very similar to those used by Potting et

12We would like to recall here the important feature of many present environmental

problems that a single source (thus also an emission/f.u.) contributes only very

little, in most cases marginally to the background exposure of any receptor.

13”PAL” stands for predicted acidifying load (copied from predicted environmental

concentration in toxicity assessment).
70

al. (1998a,b). Their acidification factors are calculated as

the share of an emission that deposits on ecosystems

with already exceeded critical loads multiplied with the

potential of the given substance to release hydrogen ions.

These characterization factors tell us what change an

emission evokes in the exceedance of already exceeded

critical loads ( ∆ PAL/CL l PAL≥CL). Huijbregts (1998)

proposes acidification factors almost similar to the ones

of Lindfors et al. (1998).

A disadvantage of the acidification factors of Lindfors et

al. and Huijbregts is that they do not account for the extent

to which the critical loads of ecosystems are exceeded (i.e.

differences in background loads above threshold do not play

a role). Practical implication of their acidification factors is

that ecosystems exposed around critical load are regarded

equally important as ecosystems exposed far above critical load. Another, more sophisticated way to account for

background load is presaged by Pleijel et al. (1997). The

characterization factors of Lindfors and Huijbregts can easily be adjusted by multiplying the change in exceedance

with a severity factor given by the existing background load

divided by the critical load for the given ecosystem (Posch

1998). Such acidification factors do, similar as the damage

based ones from Potting et al. (1998a,b), account for both

differences in ecosystem sensitivity and differences in background loads above threshold.

Huijbregts (1998) also proposes another way to calculate

acidification factors. These factors are basically similar to

those mentioned in the previous section about “Less is better”. He wants to calculate the relative contribution of a source

to exceedance of the critical load by calculating the share

of emission that deposits on ecosystems with already exceeded critical loads divided by the critical load of the given

substance. Such acidification factors do account for differences in ecosystem sensitivity, but not for differences in

background loads above threshold (see above).

“Less Is Better” Versus “Only Above

Threshold” Too Simple

The threshold discussion has been topical for already quite

some years now but seems to get stuck in a controversy

about “less is better” or “only above threshold” principle.

As shown in the previous sections, the “only above threshold” principle covers very different ways of dealing with

threshold. An important objection from the “less is better”

advocates towards the “only above threshold” and the “only

around threshold” principles, is that ecosystems exposed

below their critical loads are not accounted for in the characterization factors. That might be a justified criticism, but

it is the question whether it is a good alternative to consider

all ecosystems as equally vulnerable by disregarding their

background loads. However, most of the characterization

factors discussed above can be adjusted easily to cover

also ecosystems exposed below their critical load. It would

therefore be interesting to calculate each of the above-discussed acidification factors with the same models (for instance with the RAINS model), but to establish each of

these factors for a below, and above, and below & above

threshold situation (Table 1).

Within each principal framework of the acidification factor, the “below & above threshold factors” should logically

be a summation of the “below threshold factor” with the

“above threshold factor”. Furthermore, we expect the spatial differentiated “below & above threshold factors” to show

the same, but smoothed trend as the similar “above threshold factor”. However, it would be interesting to see whether

the acidification factors based on different principal frameworks also show the same trends.

The results of above comparisons, completed with a thorough and fair evaluation of the implications of each type of

Table 1.

Name Principal AF = “principal framework”

acidification factors framework PAL<CL PAL3CL Authors

Exceedance of CL AF=PAL X Huijbregts 1998, Lindfors et al. 1998

X Present document

X X Huijbregts 1998

Severity of exceedance AF=SDPAL*PAL/CL X Pleijel et al. 1997

X Present document, Posch 1998

X X Present document, Posch 1998

Exceedance relative to CL AF=SDPAL/CL X Huijbregts 1998

X Present document

X X Huijbregts 1998

Unprotected area AF=SD X Potting et al. 1998

(for PAL+ DPAL)/CL»1) (PAL»CL)

Ecosystem damage AF=SD(PAL+ DPAL) X Potting et al. 1998

X Present document

X X Goedkoop in present proceedings

CL stands for Critical Load, PAL stands for Predicted Acidifying Load, A stands for area of ecosystem, D stands for Damage
71

acidification factor may give fresh input to the threshold

discussion. It will for sure provide a sound basis to make an

explicit choice for either one or another type of acidification

factor.

Closing Remark

It might be clear that the whole discussion above is exemplified with acidification but is valid as well for the other

impact categories!

Acknowledgment

We like to thank Göran Finnveden for the stimulating discussions in relation to this presentation.

References

Huijbregts, M. Operational uncertainties in life-cycle impact

assessment of acidification and eutrophication; a proposal. Amsterdam (the Netherlands), Interdisciplinary

dept. of Environmental Science (IVAM) at the University of Amsterdam, not published.

ILSI. Human health impact assessment in life-cycle assessment: analysis by an expert panel. Washington DC (United

States of America), International Life Science Institute,

1996.

Lindfors, LG., M. Alkemark, C. Oscarsson and C. Spännar. A

manual for the calculation of ecoprofiles intended for third

party certified environmental product performance declarations. Stockholm (Sweden), Swedish Environmental

Institute (IVL), 1998.

Pleijel, K., J. Altenstedt, H. Pleijel, G. Lövblad, P. Grennfelt, L.

Zetterberg, J. Fejes and L-G. Lindfors. A tentative methodology for the calculation of global and regional impact

indicators in type-III ecolabels used in Swedish case studies. Stockholm (Sweden), Swedish Environmental Institute (IVL), not published.

Posch, M., P.A.M. de Smet, J.P. Hettelingh, R.J. Downing.

Calculation and mapping of critical thresholds in Europe:

Status report 1995. Bilthoven (the Netherlands), Co-ordination Center of Effects located at the National Institute

of Public Health and the Environment, 1995.

Posch, M., J.P. Hettelingh, P.A.M. de Smet, R.J. Downing.

Calculation and mapping of critical thresholds in Europe:

Status report 1995. Bilthoven (the Netherlands), Co-ordination Center of Effects located at the National Institute

of Public Health and the Environment, 1997.

Posch, M. Personal communication from M. Posch. Groningen

(the Netherlands), 1998.

Potting, J., M. Hauschild, H. Wenzel. “Less is better” and “only

above threshold”; Two incompatible paradigms for human

toxicity in life-cycle assessment? Journal of Life Cycle

Assessment, Vol. 4 (1999), Issue 1, pp 16-24.

Potting, J. and M. Hauschild. Predicted environmental impact and expected occurrence of actual environmental

impact. Part 1: The linear nature of environmental impact from emissions in life-cycle assessment. International Journal of Life Cycle Assessment, Vol. 2 (1997a),

Issue 3, pp 171-177.

Potting, J. and M. Hauschild. Predicted environmental impact and expected occurrence of actual environmental

impact. Part 2: Spatial differentiation in life-cycle assessment by site-dependent assessment of environmental impact from emissions. International Journal

of Life Cycle Assessment, Vol. 2 (1997b), Issue 4, pp

209-216.

Potting, J. and M. Hauschild. Spatial differentiation in lifecycle impact assessment; technical backgrounds (provisional title). Copenhagen (Denmark), Danish Environmental Protection Agency, forthcoming.

Potting, J., W. Schöpp, K. Blok and M. Hauschild. Comparison of the acidifying impact from emissions with

different regional origin in life-cycle assessment. Journal of Hazardous Materials, Vol. 1998a, Issue 61, pp

155-162.

Potting, J., W. Schöpp, K. Blok and M. Hauschild. Sitedependent life-cycle impact assessment of acidification. Journal of Industrial Ecology, Vol. 2 (1998b), Issue 2, pp 63-87.

Potting’s Biography

In 1991, Potting started her research in the field of lifecycle assessment and energy analysis within the Dept. of

Science, Technology and Society at Utrecht University. Her

research was case study oriented, but she made also several outstanding methodological contributions. This resulted

in an invitation by the Life-Cycle Centre at the Technical

University in Denmark to elaborate her thoughts on spatial

differentiation in life-cycle assessment. In 1996, she started

as a guest researcher at the Technical University of Denmark. She was offered here in 1997, on return from a three

month fellowship at the International Institute of Applied

System Analysis in Laxenburg in Austria, a permanent position at the Institute of Product Development that is affiliated to the Technical University of Denmark. Since 1996,

the research activities from Potting are focussed fully on

method development for spatial differentiation in life-cycle

assessment. During all her appointments, she actively participated in several international work groups (NNI/ISO,

SETAC, LCANET a/o.). At present, she is chair of the scientific task group on acidification, eutrophication and nutrient enrichment (WIA2/STG-AENE) established under the

European branch of SETAC. In all her university appointments, Potting has been actively involved in educational

matters. She initiated and elaborated several MSc-courses

and she tutored in several subjects.
72

Potting’s Overheads

Levels of sophistication in life

cycle impact assessment of

acidification

November 1998

José Potting (IPU/DTU)

Wolfgang Schöpp (IIASA)

Kornelis Blok (NWenS-UU)

Michael Hauschild (IPU/DTU)

Outline

• Problem setting

• Methods and means

• Results

• Levels of sophistication

• Conclusions

An example:

Location alternatives for copper

production

in

Albania & Belgium & Finland

Assumptions

Similar production technologies

with

Similar emission quantities per kton copper

Inventory analysis

Acidifying emissions per kg copper

SO2 ≈ 100 gram

NOx ≈ 10 gram

Assessment of “hazard potential”

(per kton copper)

SO2 ≈ 100 * 1 ≈ 100 gram SO2-equivalents

NOx ≈ 10 * 0.7 ≈ 7 gram SO2-equivalents

total ≈ 100 + 7 ≈ 107 gram SO2-

equivalents

Poor accordance between

Hazard potential and actual impact

Due to disregard of:

Emission dispersion and deposition patterns

Background depositions on receiving ecosystems

Sensitivity of receiving ecosystems
73

Traditional impact assessment tools

(like gaussian plume modelling)

High accuracy to predict acidification local

to the source (r < 50 km)

Unable to predict acidification over large

distances (r >1000 km)

RAINS

Regional Air pollution INformation System

Emission inventories

Atmospheric dispersion and deposition

Critical loads

(Optimization of emission reduction)

Emissions of SO2

 Albania Belgium Finland Sector

 11 0 3 % Fuel conversion

 35 26 35 % Industrial combustion

 2 27 24 % Industrial processes

 35 32 30 % Power plants/district heating

 13 10 6 % Domestic, agriculture

 4 5 3 % Transportation

 0 0 0 % Otherwise

100 100 100 % Total

 120 317 260 kton Total

SO2 depositions from 

Finland

Transfer-matrices from EMEP 

MSC-W at the Norwegian

Meteorological Institute

SO2 depositions from 

All 44 regions

Transfer-matrices from EMEP 

MSC-W at the Norwegian

Meteorological Institute

See next

page

Deposition of SO2 on grid x=20, Y=26 of Finland

Region Contribution Region Contribution

Austria 0.01 % Lithuania 1.77 %

Baltic sea 1.24 % North sea 0.24 %

Belarus 3.13 % Norway 0.23 %

Belgium 0.38 % Poland 9.68 %

Bohemia 0.11 % Romania 0.53 %

Bulgaria 0.14 % Russia - Kalingrad 2.84 %

Croatia 0.04 % Russia - Kola, Karelia 3.69 %

Czech republik 3.81 % Russia - Remaining areas 0.16 %

Denmark 0.97 % Russia - St. Petrusburg 3.15 %

Estonia 8.6 % Slovakia 0.61 %

Finland 25.95 % Slovenia 0.01 %

France 0.67 % Sweden 1.77 %

Germany-exBRD 13.4 % The Netherlands 0.24 %

Germany-exDDR 1.64 % Ukraine 2.41 %

Hungaria 1.21 % United Kindom 3.89 %

Ireland 0.02 % Yugoslavia 0.13 %

Italy 0.14 % Natural emissions 0.36 %

Latvia 1.52 % Non-attributable sources 5.33 %

Total 100.00 %

0

10

20

30

40

50

60

70

80

90

100

0 500 1000 1500 2000 2500

Critical Loads for actual acidity in eq/ha.yr

Cumlative % ecosystems
74

Area (in %) of unprotected 

ecosystem (depositions 

exceed critical loads)

Calculations based on:

Transfer-matrices from EMEP 

MSC-W at the Norwegian

Meteorological Institute

Critical loads provided by

CCE at the National Institute

for Effects of the Public Health

and the Environment in The

Netherlands

Logical deductions

Total emissions from a region contribute little to the

total depositions on receiving grid elements

The full emission from a separate source contributes

little to the total emissions from a region

The full emission from a separate source contributes

marginally to the total depositions on receiving grid

elements

Mathematical framework for the acidification factors

 m

( UES(E(ref)) - Σ UESs,j((1- δ)E(ref)) ) / δEs,i(ref) =

 j=1

Example for an SO2 emission from Finland

( 81671 -81278 ) / 26 =

394 / 26 =

15 ha/ton

0.15 m2 /gram

Site-dependent assessment of the acidifying impact

(per kton copper)

n z

Σ Σ Emissioni,s * Acidification Factori,s i=1 s=1

 SO2 NOx

 Albania 100 * 0.00 + 10 * 0.00 = 0 m2

 Belgium 100 * 0.01 + 10 * 0.01 = 1.4 m2

 Finland 100 * 0.15 + 10 * 0.02 = 15.2 m2

Acidification factors (in ha/ton)

Region SO2 NOx NH3

Albania 0.02 0.00 0.01

Austria 1.31 0.42 3.44

Belarus 4.65 4.54 5.72

Belgium 1.28 0.82 1.10

BosniaHerzogovina 0.15 0.04 0.06

Bulgaria 0.07 0.02 0.05

Croatia 0.30 0.12 0.17

CRZF 1.91 0.69 1.26

Denmark 5.56 2.02 5.28

Estonia 12.43 1.54 3.92

Finland 15.14 2.42 13.40

France 0.79 0.47 0.74

Germany new 2.17 0.90 1.89

Germany old 1.94 1.42 3.31

Greece 0.01 0.00 0.01

Etc.......

Interim-conclusions

• Acidification factors have been established

for 44 European regions

• The application of these factors is very simple

• The resolving power is large, while the

uncertainties are small

• Acidification factors show reasonable good

stability for changes in the reference situation
75

Recent developments/

forthcoming work

• Hogan et al. (Int. J. of LCA 1996)

• Tolle et al. (Int. J. of LCA 1997)

• Seppällä (FEI 1997)

• Potting et al. (JIE 1998)

• Norris (proposal; US-EPA 1998)

• Rabl et al. (1998)

• Pleijel et al. (proposal; not published)

• Huijbregts (proposal; not published)

•

Process oriented policy

•

•

•

Product orriented policy

Below threshold = 0 

Above threshold = 1

Below threshold = 1

Above threshold = 1

Ability for spatial differentiation

Accordance predicted&actual impact

Ability for non-defined processes

Simplicity to use

Limited requirement of data

EIA/RA

Present typical LCA

Potting et al. (1998)

Norris (1998)

Pleijel et al.

Sepällä 1997

Hogan et al. (1996)

Tolle (1997)

ExternE (1995)

• •

Levels of sophistication

• Detail of assessment?

• Uncertainties?

• Marginal or average impacts?

• Threshold evaluation?

• Anticipated indicators/endpoints?

• Type of modelling?

• ......

Final conclusions

• A considerable level of spatial detail in LCA is

possible with limited additional data

requirement from inventory analysis

• Lack of spatial differentiation results in large

uncertainties in the assessed impact

• The level of sophistication in impact

assessment may differ depending on the type

of application

• All recent developments make use of (similar)

integrated assessment models
76

Abstract

State of the art and research needs for the impact category eutrophication are discussed. Eutrophication is a difficult impact category because it includes emissions to both

air and water, both subject to different environmental mechanisms, and impacts in different types of terrestrial and

aquatic ecosystems. The possible fate processes are complex and include transportation between different ecosystems. In some recent approaches modeling of transportation of air emissions has been included. However, in general the characterization methods used lack fate modeling

and this is a limitation. Also the definition of the impact

indicator may need further research. The inclusion of other

nutrients than those typically considered should also be

investigated.

On “Thresholds”/”No Effect Levels”/

”Critical Loads” etc.

Based on two example, human toxicity and acidification,

it is suggested that ”thresholds” are always based on a choice

of what is ”acceptable”, or ”significant” or something similar

that in turn is based on value judgements. It is also suggested that the determination of “thresholds” is based on

models that often are highly uncertain. It is thus clear that

LCA is not the only science where there is an uncertainty

concerning the appropriate methods and where different

methods can give significantly different results. It is also

suggested that since the determination of ”thresholds” ultimately is based on values, the ”only above thresholds” approach should be avoided if we want to avoid values in the

characterization. It is also important to realize that impacts

can occur below ”thresholds”.

References

Finnveden, G. and Potting, J. (1999): Eutrophication as an

Impact Category - State of the Art and Research Needs.

Int. J. LCA. In press.

Finnveden, G. and Potting, J. (1999): On the nature of “thresholds” and some implications. In preparation. Provisional

title.

Finnveden’s Biography

Finnveden has a M.Sc in Chemical Engineering and PhD

in Natural Resources Management. Currently working at fms

(Environmental Strategies Research Group), which is a cooperation between the Department of Systems Ecology at

the Stockholm University and the National Defence Research

Establishment (FOA) in Stockholm, Sweden. Has been

working with Life Cycle Assessment since 1990, both on

case studies and development of methodology, e.g. the

development of the Nordic Guidelines on Life Cycle Assessment. Active in international working groups, e.g. the ”International Forum on Life Cycle Assessment and Integrated

Solid Waste Management” and the SETAC-Europe working

group on Life Cycle Impact Assessment where he currently

is chairing the task group on normalization and weighting.

Eutrophication – Aquatic and Terrestrial –

State-of-the-Art

Göran Finnveden

fms (Env. Strategies Research Group)

National Defence Research Establishment (FOA)

José Potting

Institute of Product Development

Technical University of Denmark
77

Finnveden’s Overheads

Eutrophication -

Aquatic and Terrestrial -

State of the Art

Göran Finnveden

fms and Stockholm University

José Potting

Institute of Product Development

(affiliated to the Technical University of Denmark)

Eutrophication is a difficult category

• Emissions to air and water

• Impacts in different types of terrestrial and aquatic systems

• Fate processes are site dependent

• Impacts are site dependent

– different background loads

– different nutrients may be limiting

– different sensitivities of ecosystems

Inventory parameters normally

assigned to ”Eutrophication”/

”Oxygen depletion”

• N-emissions to air

• Macro-nutrients (N and P) to water

• Organic material to water

Other nutrients which locally could be

limiting are normally not considered

Limiting nutrients

• Terrestrial systems, often N

(at least for terrestrial ecosystems in Europe and North

America)

• Aquatic systems

– freshwater often P-limited

– seawater often N-limited

– coastal and brackish water, P or N or both

...but...

• The picture of ”limiting nutrient” is often

simplified because

– the limiting nutrient may change over the year

– the limiting nutrient may change over the years

– the balance between nutrients is often important

– nutrients may be transported

• Therefore, the contribution from an emission to

eutrophication is always(?) larger than 0

Examples of possible fate of N

emitted to air

• It can be deposited on surface water

• It can be deposited on (natural) soil or vegetation

– it can be leached out to surface water

• the fraction being leached out depends on site specific aspects

(Varies between approx 5-80 % in Europe).

– it can be taken up by growing vegetation, contributing

to terrestrial eutrophication

• after degradation it can be taken up again, leached out,

exported with harvested timber, immobilised in the soil or

denitrified
78

...fate...

• It can reach strictly P-limited waters

– it can be lost from the system (e.g. by denitrification)

– it can be transported to other waters

• It can reach waters where N contributes to

eutrophication

– after degradation it can be taken up again, transported

to other waters, exported with fish, or denitrified

Three methods were suggested in

1992-93

Method 1 (Jensen et al, 1992)

• No aggregation at all

• Four subcategories

– emissions of N to air

– emissions of N to water

– emissions of P to water

– emissions of BOD

...three methods...

Method II (Heijungs et al, 1992)

• Complete aggregation into one category

• Definition of the effect: Biomass production.

• All emissions of N (air and water), P and organic

material is assumed to contribute once and to the

same impact.

• Weighting factors based on Redfield ratio:

C:N:P = 106:16:1

...three methods...

Method III (Finnveden et al, 1992 and Samuelsson, 1993).

• Scenario based approach

• Terrestrial and aquatic systems are separate

• Terrrestrial systems: N-emissions to air.

• Aquatic systems:

– Definition of the effect: Oxygen consumption

– Weighting factors based on Refield ratio

– Emissions can only contribute once, but sometimes

not at all

...three methods...

Method III - continued

• Four different scenarios for aquatic systems:

– P-limited: Aggregation of P and org. mat.

– N-limited: Aggregation of N to water and org. mat.

– N-limited+ air: Aggregaton of N to water and air and

org. mat.

– Max: Aggregation of P and N to water and air and org.

mat. (Identical to Heijungs et al, 1992)

Developments 1993-1996

• Demands for site dependent factors

• Nice student work from ETH suggesting site dependent

factors reflecting sensitivity and limiting nutrient.

• Method IV (Hauschild and Wenzel, 1996).

– Similar to methods II and III

– Organic material excluded

– Either N and P separate or combined

• Method V (Tolle, 1997)

– Regional scaling factor (1-9) based on present loadings
79

SETAC-Europe working group

on LCIA (1996)

• Asking for adaption of earlier approaches:

– Several subcategories: terrestrial systems, smaller

aquatic systems, larger aquatic systems?

– Other nutrients?

– Sensitivity of receiving environments

– Transportation

• For emissions to air, similar models as for acidification

• For water emissions: ?

Recent work by Seppälä

• Two impact categories for aquatic ecosystems:

• Oxygen depletion: BOD

• Aquatic eutrophication

– Definition of the effect: Increased production based on

Redfield ratio

– For water emissions of N and P, site specific transport

and effect factors were determined by experts.

– For air emissions of N, transport factors were based on

EMEP-data (in this case, 6-7% of emitted N are

deposited in N-sensitive areas)

Recent work by Lindfors et al and

Pleijel et al

• Aquatic oxygen depletion for aquatic ecosystems

– Definition of the effect: oxygen depletion

– Weighting factors based on Redfield ratio

– Site specific assessment in which emissions are only

considered for receiving environment where pollutants

have an effect as determined by expert judgement

Recent work by Potting et al

• So far, only emissions of N to air for terrestrial

ecosystems

– (research on water emissions is ongoing)

• Transport from EMEP-models

• Definition of the effect: A marginal approach:

The area of ecosystem that becomes unprotected as a

result of that emission

Discussion issues - effects

• Distinction between terrestrial and aquatic systems?

• Definition of the effect

– Aquatic systems: Oxygen consumption/biomass

production?

* ?

– Terrestrial systems:

* Area ”unprotected” which receives emission?

* Amount emitted into ”unprotected” area?

* ?

• Only above thresholds, or both below and above, perhaps

in separate subcategories?

• Marginal or average approach?

Research issues - fate and target modelling

• Emissions to air

Integrated assessment models?

”Background” concentrations

Target sensitivity

Leaching of nutrients?

N deposited on land can be leached out and reach Nsensitive area (possibly after transport)

• Emissions to water?

Research project? Study some site specific cases in

order to get an understanding of the variability.

Role for expert judgements?
80

Research issues - spatial info

• Spatial differentiation?

• How do we make optimal use of spatial

information?

• Can we develop spatially differentiated and

not differentiated characterisation factors

which are compatible?

• Requirements towards inventory.....

On ”thresholds”/no effect

levels”/”critical loads” etc

Göran Finnveden

fms and Stockholm University

with contributions from

José Potting

Technical University of Denmark

Suggestion

• ”Thresholds” are always based on a choice of

what is ”acceptable” or ”significant” or similar.

The choice of an ”acceptable” impact is based on

values.

• Determination of ”thresholds” are based on

models which are uncertain

Two examples:

acidification and human toxicity

Acidification

• Critical loads often defined as

”A quantitative estimate of an exposure to one or

more pollutants below which significant harmful

effects on specified sensitive elements of the

environment do not occur according to present

knowledge”.

(Nilsson and Grennfelt, 1988)

Acidification....

Results from ”Lake Gårdsjön”, Sweden,

Andersson et al (1998):

Comparison of 8 more or less ”official”

methods for calculating CL:

CL for S varies between 1-20 kg S/ha yr

CL for N varies between 8-15 kg N/ha yr

Human toxicity

• Dose-response curves for individuals may

or may not have a threshold

• Dose-response curves for populations do

not have a threshold.

”Thresholds” are always based on a choice

of a ”acceptable” risk, such as 1:106
81

Human toxicity...

Differences in model-derived estimates of virtually safe doses

at 10-6 risk level, relative to the one-hit model

(Casarett and Doull’s Toxicology, based on other sources)

CarcinogenOne-hit Linearized

multistage

Weibull Multihit

Vinyl

chloride

1 1 1*10-7 2*10-8

Aflatoxin 1 20 1000 8000

Conclusions/discussion

• LCA is not the only science where there is

an uncertainty concerning the appropriate

methods and where different methods can

give significantly different results

Conclusions/Discussion...

• If we want to avoid values in the characterisation, we

should perhaps avoid the use of ”only above thresholds”

• Impacts can occur below ”thresholds”

– especially if the threshold is for a single substance while it

contributes to a cocktail of pollutants for which no threshold has

been defined

• Suggestion: if thresholds are used, include both above and

below thresholds in the impact assessment, but make a

distinction

• We can use thresholds as a basis for characterisation

factors and use the science behind to define the effect

• Strive for a good balance between the additional

information gained and the new uncertainties introduced
82

Guntra Aistars

University of Michigan

Dana Building 430 E. University

Ann Arbor, MI 48109-1115

tele:734-764-1412

fax:734-647-5841

email: guntra@umich.edu

Jane Anderson

BRE

Watford, Herts, WD2 7JR United Kingdom

fax: 44-1923-664 084

email: andersonj@bre.co.uk

Jane Bare

U.S. EPA (MS-466)

Cincinnati, OH 45268

tel: 513-569-7513

fax:513-569-7111

email: bare.jane@epa.gov

Angeline de Beaufort-Langeveld

FEFCO-GO-KI

Groeterweg 13

6071 ND Swalmen

tel. 0475 - 501 014

fax. 0475 - 504 478

e-mail: beulang@wxs.nl

Mark Buridard

International Iron & Steel

Rue Colonnel Boury 120

1140 Brussels Belgium

email: lcaletter@iisi.be

Paul Butterworth

International Iron & Steel Institute

email: butterworth@IISI.be

Andreas Ciroth

Technische Universität Berlin

Institut für Technischen Umweltschutz

Abfallvermeidung und Sekundärrohstoffwirtschaft

Sekt. KF 6

Strasse des 17. Juni 135

D-10623 Berlin

tel.: 49-30-314 79564

fax: 49-30-314 21720

email: ciroth@itu302.ut.tu-berlin.de

Garrette Clark

UNEP

Tour Mirabeau – 39-43,

Quai André Citroen

75739 Paris Cedex 15 France

Email: garrette.clark@unep.fr

Pierre Crettaz

Ecole Polytechnique Federale de Lausanne

DGR-IATE-HYDRAM

CH-1015 Lausanne Switerland

tel.:41-21-693 3729

fax:41-21-693 3739

email: pierre.crettaz@epfl.ch

Mary Ann Curran

U.S. EPA (MS-466)

Cincinnati, OH 45268

tel: 513-569-7782

fax:513-569-7111

email: curran.maryann@epamail.epa.gov

Suzy Edwards

BRE

Watford, Herts, WD2 7JR United Kingdom

fax:44-1923-664 084

email: edwardss@bre.co.uk

Tomas Ekvall

Chalmers Industriteknik

Chalmers Teknikpark

S-41288 Göteborg Sweden

tel. 46-31-772 4336

fax. 46-31-827 421

e-mail: tomas.ekvall@cit.chalmers.se

Göran Finnveden

Fmw and Department of Systems Ecology

Box 2142

SE-103 14 Stockholm Sweden

tel.: 46-8-402 3827

fax: 46-8-402 3801

email: finnveden@fms.ecology.su.se

Marina Franke

Proctor & Gamble

Global Technical Policy/Environmental Management

Postfach 5825, Industriestrafe 30-34

D-65733 Eschborn, Tanus

tele:(06196) 89-4723

fax:(06196) 89-6648

email: franke.m@pg.com

Jean-Paul Fretiere

European Product Responsibility Manager

3M France

Boulevard de l’Oise

95006 Cergy Pontoise Cedex

tele:33/130318107

fax:33/130318192

email: jfretiere@mmm.com

Appendix

List of Participants
83

Bernard Gonzales

3M

U.S.A.

Email: bagonzales@mmm.com

Mark Goedkoop

PRé Consultants B.V.

Plotterweg 12

NL-3821 BB Amersfoort Netherlands

tel.: 31-33-455 5022

fax: 31-33-455 5024

email: goedkoop@pre.nl

Jeroen Guinée

Centre of Environmental Science

P.O. Box 9518

NL-2300 RA Leiden Netherlands

tel.: 31-71-527 7477

fax: 31-71-527 7434

email: guinee@rulcml.leidenuniv.nl

Michael Hauschild

Department of Manufacturing Engineering

Technical University of Denmark

Building 424

DK-2800 Lyngby Denmark

tel.: 45-45-254 664

fax: 45-45-935 556

email: mic@ipt.dtu.dk

David Heath

ICI

email: david_heath@ici.co.uk

Edgar Hertwich

University of California, Berkeley

310 Barrows Hall #3050

Berkeley, CA 94720-3050

tele:510-642-8853

fax:510-642-5815

email: hertwich@socrates.berkeley.edu

Patrick Hofstetter

Umweltnatur- und Umweltsozialwissenschaften (UNS)

ETH-Zentrum HAD F1

CH-8092 Zuerich Switzerland

tel: 41-1-632 49 78

fax: 41-1-632 10 29

email: hofstetter@uns.umnw.ethz.ch

Maarten ten Houten

TNO-Institute of Industrial Technology

P.O. Box 5073

NL-2600 GB Delft Netherlands

tel.: 31-15-260 8828

fax: 31-15-260 8756

email: m.tenhouten@ind.tno.nl

Mark Huijbregts

IVAM, University of Amsterdam

Nieuwe Prinsengracht 130

NL-1018 VZ Amsterdam Netherlands

tel.: 31-20-525 6263

fax: 31-20-525 6272

email: m.huijbregts@frw.uva.nl

Norihiro Itsubo

Japan Environmental Management Association for Industry

Hirokohji Bldg. Ueno 1-17-6, Taitoh-ku

Tokyo Japan 110-8535

tel.: 81-3-3832 0515

fax: 81-3-3832 2774

email: itsubo@jemai.or.jp

Olivier Jolliet

Institute of Soil and Water Management

EPFL-IATE-HYDRAM

CH-1015 Lausanne Switzerland

tel..: 41-21-693 7011

fax: 41-21-693 7084

email: olivier.jolliet@epfl.ch

Henry King

Unilever

email: henry.king@unilever.com

Walter Klöpffer

C.A.U. GmbH

Daimlerstraße 23

D-63303 Dreieich Germany

tel.: 49-6103-983 28

fax: 49-6103-983 10

email: C.A.U.@t-online.de

Helena Mälkki

VTT Chemical Technology

P.O. Box 14031

FIN-02044 VTT Espoo Finland

tel.: 358-9-4566442

fax: 358-9-4567043

email: Helena.Malkki@vtt.fi

Llorenç Milà i Canals

Universitat Autònoma de Barcelona

Unitat de Química Física, Edifici Cn,

Bellaterra

Barcelona 08193 Spain

tel.: 34-3-581 21 64

fax: 34-3-581 29 20

email: llorenc@klingon.uab.es

Karl-Michaesl Nigge

Rheinweg 27

D-53113 Bonn Germane

tel. + fax: 49-228-233 763

email: Karl-Michael.Nigge@dir.de
84

Leo De Nocker

VITO

Boeratang 200

B-2400 Mol Belgium

32-14-334 885

fax: 32-14-321 185

email: denockel@vito.be

Gregory Norris

Sylvatica

147 Bauneg Beg Hill Rd.

Suite 200

North Berwick, ME 03906

Tel: 207-676-7640

Fax: 207-676-7647

email: norris@sylvatica.com

Stig Irving Olsen

Institute for Product Development

Technical University of Denmark

Building 424

DK-2800 Lyngby Denmark

tel.: 45-45-254 611

fax: 45-45-935 556

email: sio@ipt.dtu.dk

Willie Owens

Procter & Gamble

5299 Spring Grove Ave.

Cincinnati, Ohio 45214

Tel: 513-627-8183

Fax: 513-627-8198

email: owens.jw@pg.com

David Pennington

ORISE Post Doctoral Researcher

U.S. EPA (MS-466)

Cinncinnati, OH 45268

tel: 513-569-7618

fax:513-569-7111

email: pennington.david@epamail.epa.gov

Josë Potting

Technical University of Denmark

c/o Bo Pedersen Weidema

IPU, Life Cycle Engineering Group

Building 40311

DK-2800 Lyngby Denmark

tele:4545254677

fax:4545935556

email: jp@ipt.dtu.dk

Steven Rolander

SAIC

11251 Roger Bacon Dr.

Reston, VA 20190

Tel: 703-318-4605

Fax: 703-736-0826

email: rolanders@saic.com

Erwan Saouter

Procter & Gamble, Environmental Science Department

100 Temselaan

B-1853 Strombeek Bever Belgium

tel.: 32-2-4562491

fax: 32-2-4562845

email: saouter.e@pg.com

Guido Sonnemann

Chemical Engineering Department

Universitat Rovira i Virgili

Carretera de Salou s/n

E-43006 Tarragona Spain

tel.: 34-977-559 618

fax: 34-977-559 667

email: gsonnema@etseq.urv.es

Bengt Steen

CPM

Chalmers University of Technology

SE-412 96 Gothenburg Sweden

tel.: 46-31-772 2177

fax: 46-31-772 2172

email: bengts@vsect.chalmers.se

Duane Tolle

Battelle Columbus Division

Columbus, OH 43201

Tel.:614-424-7591

Fax: 614-424-3404

email: tolled@battelle.org

Arnold Tukker

TNO-STB

P.O. Box 541

NL-7300 AM Apeldoorn Netherlands

tel.: 31-55-5493907

fax: 31-55-5421458

email: tukker@stb.tno.nl

Helias A. Udo de Haes

Centre of Environmental Science

P.O. Box 9518

NL-2300 RA Leiden Netherlands

Tel.: 31-71-527 7488

Fax: 31-71-527 5587

email: udodehaes@rulcml.leidenuniv.nl

Bo Weidema

Technical University of Denmark

Inst. for Product Development

Building 424

2800 Lyngby Denmark

email: bow@ipt.dtu.dk
85

Keith Weitz

Research Triangle Institute

3040 Cornwallis Rd.

P.O. Box 12194

RTP, NC 27709

Tel: 919-541-6973

Fax: 919-541-7155

email: kaw@rti.org

Henrik Wenzel

IPU-DTU

Technical University Denmark

Building 424

D-2800 Denmark Denmark

tel.: 45-45-254 663

fax: 45-45-935 556

email: hwc@ipt.dtu.dk