An Activity-Based Life-Cycle Assessment Method - the Systems ...

Proceedings of DETC’97: 

1997 ASME Design Engineering Technical Conferences 

September 14 - 17, 1997 - Sacramento, California 

DETC97/DFM-4376 

AN ACTIVITY-BASED LIFE-CYCLE ASSESSMENT METHOD 

Jan Emblemsvåg 

Graduate Research Assistant, 

Norwegian Research Council and 

Fulbright Fellow 

ABSTRACT 

In this paper, a new activity-based method for the concurrent 

assessment and tracing of costs, energy consumption and waste 

generation for usage in Life-Cycle Design is presented. The 

method is based on Activity-Based Costing (ABC). By 

utilizing ABC we are able to assess and trace accurately 

overhead - and direct costs, energy and waste generation.. The 

inherent uncertainty is modeled in terms of fuzzy numbers, but 

solved numerically using the Monte Carlo simulation 

technique, which allows us to perform a sensitivity analysis. 

This enables us to not only estimate the costs, energy 

consumption and waste generation, but also trace the largest 

contributors. The capability to trace the contributors is perhaps 

the most significant feature - especially when the method is 

employed for design. To illustrate the usage of this method, a 

simple case study is provided in which we explore the life-span 

costs, energy consumption and waste generation of a oil/gas 

exploration platofrm supply vessel operating in the North Sea. 

KEYWORDS: Activity-Based Costing, Life-Cycle Design, 

Uncertainty, Monte Carlo Simulation and Sensitivity Analysis. 

NOMENCLATURE 

Activity driver: A measure of the consumption of an 

activity by the assessment objects. 

Assessment object: Any customer, service, project or 

product (design) for which separate cost and revenue and/or 

energy consumption and/or waste generation assessments are 

needed. 

Cost consumption intensity: Unit-price of a cost driver 

(Cooper 1990). E.g. USD per direct labor hour. 

Cost driver: Any factor that causes a change in the cost of 

an activity (Brinker 1997). Direct labor is an example of a cost 

driver. In conventional costing systems, only unit-level ‘cost 

drivers’ are found and these are called allocation bases. We 

use cost drivers as a collective term. 

Cost element: Amount paid for a resource consumed by an 

activity; it is included in a cost pool (Brinker 1997). 

Cost pool: A specified grouping of cost elements. For 

example, in (Brinker 1997) an activity cost pool is defined as a 

grouping of all cost elements associated with an activity. 

Energy consumption intensity: Unit-energy 

consumption of an energy driver. For example Joule per 

machine hour. 

Bert Bras 

Assistant Professor 

Systems Realization Laboratory 

The George W. Woodruff School of Mechanical Engineering 

Georgia Institute of Technology 

Atlanta, GA, 30332 

USA 

Energy driver: Any factor that causes a change in the energy 

consumption of an activity. 

Resource element: An economic, energy related or waste 

related element that is used in the performance of activities. 

Resource driver: A measure of the quantity of resources 

consumed by an activity, e.g. the percentage of total square feet 

of space occupied by an activity (Brinker 1997). 

Resource pool: A specified grouping of resource elements. 

For example, an activity resource pool is defined as a grouping 

of all resource elements associated with an activity. 

Waste generation intensity: Unit-waste generation of a 

waste driver (an extension of cost consumption intensity into 

waste generation). For example Waste Units per welding hour. 

Waste driver: Any factor that causes a change in the waste 

generation of an activity (see Waste element). 

Waste element: An element of waste generated in the 

consumption of activities, e.g., fuel consumption is a waste 

driver and CO 2, CO, SO 2 and NO X are the waste elements. 

Waste pool: A specified grouping of waste elements. For 

example, an activity cost pool is defined as a grouping of all 

waste elements associated with an activity. 

1 . 0 OUR FRAME OF REFERENCE 

1 . 1 Motivation 

It is generally agreed that environmental considerations 

must cover a product’s entire life-cycle and that a holistic, 

systems-based view provides the largest capability for reducing 

environmental impact of both products and associated processes. 

The environmental aspect of product life-cycles has become 

more and more important in recent years as our environment is 

deteriorating. To turn around this negative development, we 

must produce goods and services in a sustainable fashion. 

Already, in the U.S., a lot of attention is being given to 

Pollution Prevention and minimization of waste. 

Managing energy consumption, however, is just as 

important for good pollution (waste) control as managing 

pollutants (waste). Energy is one of the driving forces in socioeconomic 

development (Olsson 1994) and on a worldwide basis, 

1 Copyright © 1997 by ASME

energy constitutes 57% 1 of all CO 2 emissions into the 

atmosphere (Fowler 1990). 

The most comprehensive tools to be used to analyze the 

environmental impact of products are the so-called eco-balances 

and life cycle analyses/assessments (LCA). LCA is a method in 

which energy and raw material consumption, different types of 

emissions and other important factors related to a specific 

product are measured, analyzed and summoned over the entire 

life-cycle from an environmental point of view. The strength of 

LCA is the (potential) broadness of scope and depth of 

evaluation. However, designers find it almost impossible to 

practically work with LCAs, because the tools have a tendency 

to be as complex as the issue under investigation. Also, the 

LCA methods have not (yet) been fully standardized and a 

number of different weighting and assessment schemes exist 

which may cause ambiguity. Furthermore, LCAs do not 

capture the traditional way of management using economic 

indicators. 

1 . 2 Our Combined Environmental and 

Economical Assessment Method Based on ABC 

In our opinion, a life-cycle assessment method should 

capture both the environmental and economic impact of a 

product to provide the most useful feedback to designers, 

engineers, and managers. In order to achieve such an integrated 

economical and ecological assessment approach, we believe that 

the use of an Activity-Based Costing approach may overcomes 

some of the difficulties associated with conventional LCA 

tools, e.g., the cumbersome amount of work involved, the 

usage of non-comparable units and the lack of common 

standards. Our goal is to model all the aspects of product 

realization along with the associated economical transactions for 

the entire life-cycle. The foundation for this work has been 

presented in e.g. (Bras and Emblemsvåg 1996). In this paper, 

we build upon the previous work and show specifically the 

following: 

• How our new ABC based method can be used not only for 

life-cycle costing (as it was intended/invented for), but also 

for life-cycle assessments (LCAs) of environmental impact 

in terms of 

1. waste generation and 

2. energy consumption. 

• An example of how the method can be implemented using 

an UT 705 Platform Supply Vessel (PSV) is presented. 

This specific type of PSVs was designed by Ulstein 

International AS in Norway. The vessel is capable of: 

1. Transporting pipes, cement, equipment and goods to 

and from pipeline barges, oil rigs and ships. 

2. Loading and laying beside a pipeline barge under 

North Sea conditions with approximately 4.6 meter 

high waves and a tidal current of roughly 3.5 knots. 

As mentioned earlier, we assume three aspects of product 

realization in this paper; 1) costs and revenues, 2) energy 

consumption, and 3) waste generation. In the following three 

sections we will discuss these three aspects. 

2 . 0 ACTIVITY-BASED COSTING 

2 . 1 Motivation for Activity-Based Costing 

The strength of Activity-Based Costing with Uncertainty 

(referred to as ACU) described in (Emblemsvåg and Bras 1994; 

1 This number was calculated by the U.S. Environmental Protection Agency 

and applies to the USA. The actual figure may vary from country to country 

depending on its energy mix. 

Bras and Emblemsvåg 1996) is the combination of Activity- 

Based Costing (ABC) and the modeling of uncertainty as 

continuous and discrete probability distribution. The usage of 

ABC is gaining more and more ground on conventional costing 

systems (Cooper 1990; Keoleian and Menerey 1994) - initially 

due to more correct cost assessments but more lately due to the 

improved capability of tracing the costs. This tracing capability 

is enhanced in our method by the usage of uncertainty and 

sensitivity analysis. The two key differences between ABC and 

conventional costing systems are as follows: 

• In an ABC system it is assumed that a cost object consumes 

activities, while in a conventional system it is assumed that 

a cost object consumes resources. 

• An ABC system utilizes cost drivers at several levels (unit-, 

batch-, product- and factory level), while a conventional 

system uses only unit level characterizations, called 

allocation bases. An allocation base corresponds to a unitlevel 

cost driver. 

Because of these differences, ABC can handle overhead costs far 

better than conventional costing systems, and since the portion 

of overhead costs is increasing in most industries - the focus on 

overhead costs is also increasing. For in-depth discussions of 

ABC see (Cooper 1990; O'Guin 1990; Raffish and Turney 

1991; Turney 1991) A motivating example for use in an 

environmental context is found in (Brooks, Davidson et al. 

1993). For the same reasons that an activity-based costing 

method is preferred to a conventional costing method, activitybased 

methods for handling energy consumption and waste 

generation are, in our opinion, also better than more 

conventional methods. 

2 . 2 Inclusion of Uncertainty 

One of the challenges of design is the ‘inherent 

uncertainty’. There are simply no ways of discarding the 

uncertainty in design; we must therefore include it. In our 

method, the uncertainty is handled by modeling the uncertainty 

as fuzzy numbers which has two advantages: 

• Fuzzy numbers can be used with or without hard data. 

Ordinary statistics, however, needs quite a large sample of 

hard data. It goes without saying that the more relevant hard 

data, the better. 

• The usage of fuzzy numbers allows us to customize 

uncertainty distributions ( e.g. you can cut off a normal 

distribution anywhere you like). This is not possible in 

ordinary statistics. 

The Monte Carlo simulation technique is employed to solve the 

model and to find numerically how the assumption cells (where 

the fuzzy numbers are modeled) affect the forecast cells. This 

simulation is performed by the Crystal Ball ® software, which is 

an extension on top of Microsoft Excel ® software. For more 

information regarding how to include uncertainty see 

(Emblemsvåg and Bras 1994; Bras and Emblemsvåg 1996). 

3 . 0 ACTIVITY-BASED ENERGY 

CONSUMPTION ASSESSMENTS 

It is very simple to modify the ACU method and create an 

Activity-Based Energy Consumption method. Rather than 

speaking about cost drivers and cost consumption intensities, 

we can introduce energy drivers (see Nomenclature) and 

associated energy consumption intensities to perform energy 

assessments. The use of energy drivers is identical to the use of 

cost drivers. The reason we can do this is that a) energy can be 

measured in terms of Joules [J] just as costs can be measured in 

terms of [$] and b) energy - like costs - is incurred as a result of 


an assessment object’s consumption of activities. The 

uncertainty is handled identically as for the costing model. 

4 . 0 ACTIVITY-BASED WASTE GENERATION 

ASSESSMENTS 

Analogous to cost and energy drivers, we can define a waste 

driver (see Nomenclature). However, there is no obvious single 

value or clear unit as for energy (Joules) or cost (dollars) that 

can be used as a waste generation intensity (see Nomenclature) 

associated with a waste driver because there are many different 

materials, and there are many different ways these materials 

affect the environment. Hence, a single valued index for 

assessing the “cost” or impact of various materials needs to be 

defined to make our method capable of an activity-based waste 

assessment. Once a waste driver and the associated waste 

generation intensity - represented by the single valued waste 

index - are established, the very same activity-based approach 

can be applied. However, what is an appropriate index? In the 

following sections, we present a short discussion on existing 

indices (Section 4.1) and we present a recently developed index 

(section 4.2). 

4 . 1 Short Discussion on Indicators 

In the literature there are many different indices and 

indicators presented, but as thoroughly discussed in (Ayres 

1995) they suffer from two serious problems: 

1) Data deficiency, i.e. as a lot of data is missing, because it is 

a new field, This is a problem for all analyses that deal with 

energy and waste assessments. Our new index (see Section 

4.2) has the same problem, which will be evident from the 

example presented in this paper. However, this problem 

will generally disappear as more case studies and data 

become available. Already we see more case study material 

from LCAs become publicly available, increasing the 

amount of data one can draw upon. 

2) The usage of non-comparable units of measurement makes it 

impossible to compare the results from one analysis to the 

results from another analysis. An example of this is the 

Eco-Indicator used in the SimaPro software from Pré 

Consultants. The problem is that the weights and criteria 

used are questionable, thus leaving a lot of unanswered 

questions and political debate. 

Especially the (political) issue of weighting the different noncomparable 

units has led us to develop a new index that uses 

comparable units of measurement. It should be noted that our 

activity-based approach works with any single-numbered index 

or indicator. 

4 . 2 A New Waste Index 

In developing our new Waste Index, we used the following 

basic assumption: 

Any substance in a sufficient amount beyond its natural 

amount in a specified control volume (environment) 

can be considered waste (pollution). 

With ‘Waste’ and ‘Control Volume’ we mean the following: 

• ‘Waste’ is all unwanted material created by the consumption 

of an activity. The material may be any of the following 

types (radioactive or not): biological entities or tissue, 

solids, liquids and/or gases. 

• The ‘Control Volume’ is the geographical area of the 

environment affected either by the generated waste directly or 

by the substances the generated waste is decomposing into. 

We define our Waste Index (WI) as follows: 

WI CV ⎛ Waste R⋅T ⎞ ⎛ 

N R⋅T ⎞ 

= ⋅⎜ 

⎟= C⋅⎜ ⎟ 

CV ⎝ A ⎠ ⎝ A ⎠ 

System 

N 

The parameters that determine the Waste Index (WI) are: 

1. Natural amount A N [kg] of the generated waste or 

substances into which the generated waste would 

decompose in the specified control volume. Important 

tools in this context are chemical mass balance equations 

and thermodynamics, as pointed out in (Ayres 1995) as 

well. If the generated waste (or the substances the generated 

waste will decompose into) does not occur naturally in the 

control volume, then AN is set to one (1). 

2. Released amount of the generated waste - R [kg/h]. If the 

released amount is of no or negligible importance the value 

is set to one (1), e.g. in the case of corrosion of iron where 

the amount of iron is almost irrelevant. 

3. Estimated time the release will af fect the control volume - 

T N [h]. This time is measured from the very first emission 

to the time when the effect of the emissions is gone - that 

is, the time the control volume needs to achieve balance 

again. In cases where the generated waste is not degradable 

the TN is set to one (1). This special case is rare. 

4. Control volume ratio - C [-]. Obviously, the different 

parameters must be estimated in the same control volume. 

Thus, for a complex system the largest control volume 

encountered will be the determining control volume. We 

therefore scale the different control volumes by dividing the 

volume of the largest control volume for the generated 

waste element ( CVWaste ) by the volume for the control 

volume for the entire system ( CVSystem ) (see equation 1). 

The notion of control volumes is extremely important 

because it allows us to quantify continuously the 

importance of the geographical areas of impact. In many 

LCAs the only distinction is between ‘local’, ‘regional’ and 

‘global’ impact areas. This discrete qualitative method is 

too crude and ambiguous, and it increases the problem of 

non-comparable studies, in our opinion. 

Ideally, the Waste Index (WI) should approach zero and the 

larger the index, the more out of balance the control volume is 

brought by the product . Furthermore, one unit of the Waste 

Index is for simplicity denoted Waste Unit (WU) because the 

waste index is dimensionless. 

The reason for choosing one (1) as default values for AN, R 

and TN is to remove the effect of the parameter from the index. 

This is an important feature of the index because it allows us to 

handle special cases as well as the more common situations 

where all parameters are used. E.g., if we release iron to a river, 

we can assume that the degradation process for the iron will be 

corrosion. Due to the fact that the corrosion speed of iron is 

almost independent of the quantity we set R to 1 kg/h. The 

value of the index will therefore only depend of the geographical 

area (control volume), the corrosion speed and the natural 

amount of iron in the riverbed. 

If one wants to estimate the waste index for an activity, the 

following procedure applies: 

1. Measure all waste elements [kg/year] and compute the 

Waste Index for each element using equation (1). 

3 Copyright © 1997 by ASME 

N 

N 

(1)

2. Sum up the WIs for all the waste elements that belong to a 

waste driver. This value is the Waste Index of the waste 

driver. I.e., the waste index is the generation intensity of 

the waste driver. An example is given in Table 3. 

3. Sum up the WIs for all the waste drivers that belong to an 

activity. This value is the desired activity Waste Index. 

4. Sum up the WIs for all activities belonging to a product 

and/or (life-cycle) process in order to identify the totak 

Waste Index for the product and/or process under 

investigation, i.e., the assessment object. 

The Waste Index is a simple relative index. Some unresolved 

issues, however, are as follows: 

• The index can not handle biological releases (bacteria etc.), 

because this is a very complicated issue due to reproduction 

and so forth. However, for most products in mechanical 

engineering this is of no concern. 

• The index is not capable of detecting special health risks for 

humans. E.g., dust in the air may have a very severe local 

health risk to humans, but the index will not be large 

compared to the index of CO2 releases. The reason is that 

the dust effect is so local compared to the global CO2 effects. In other words, at this stage, the index is not 

handling physical (e.g. dust) releases well. We will 

therefore only use this index for tracking chemical (e.g. 

CO2) releases and their relative impact on the environment. 

More case studies need to be undertaken to see how well 

special cases are handled. 

• The Waste Index does not tell anything about how the 

emissions will affect the environment, but in our opinion 

this is more an advantage than a draw back because it will 

eliminate the political issues related to environmental 

indices. The problem with many indices is that they try to 

assess how the emissions affect the green house effect, 

ozone depletion and so forth, but these systems are very 

complex and many doubt these indices, claiming that more 

evidence is needed. Our index simply states that there will 

be an impact, and its relative severeness. 

We believe that our new waste index represent an improvement 

compared to the indices we are aware of despite the preceding 

issues. The main positive aspects of our index are as follows: 

• Our waste index (WI) is a) consistent in use of units and b) 

comparable from product to product - system to system. 

This is a major problem for all the current LCA techniques 

according to (Ayres 1995). 

• Our Waste Index also blends easily into an Activity-Based 

Life-Cycle Assessment as mentioned earlier. We can 

therefore have a single activity-based method that covers: 

1. Economic issues, 

2. Energy issues and 

3. Pollution (material) issues. 

According to the EPA, this is all that is needed to 

completely describe a process, for example a manufacturing 

process. Thus, we have one method that handles all the 

process related issues in product realization. This is a great 

advantage because it is unlikely that most companies will 

have the resources to learn three different methods; it is far 

more likely that they will prefer to learn only one method. 

• It also meets the four socio-ecological principles of The 

Natural Step (Det Naturliga Steget) organization (founded 

by the Swedish oncologist Dr. Karl-Henrik Robert in 1989) 

that must be fulfilled to create a sustainable society: 

1) Substances from the lithosphere (earth’s crust and 

mantle) must not systematically accumulate in the 

ecosphere. This is taken care of by the waste index as 

the T N will become large as the capability of the 

ecosystem to handle the releases deteriorates. 

2) Society-produced substances must not systematically 

accumulate in the ecosphere. Here the T N again will 

become larger and larger. 

3) The physical conditions for production and diversity 

within the ecosphere must not systematically be 

deteriorated. Again, the T N will capture this, because the 

T N should be calculated using thermodynamic and 

chemical models. 

4) The use of resources must be effective and just with 

respect to meeting human needs. This will be ensured as 

the waste drivers are traced effectively and thereby 

allowing proper usage of resources. Whether the usage 

of resources is just or not is more a political and ethical 

issue and therefore cannot be captured by the index. 

Because the our assessment method is activity-based we can also 

handle ‘overhead waste’ that is pollution that is not directly 

related to the product realization process, but related to, say, 

running the corporate head office, and we can also find out 

which products trigger most waste. The same theory is valid 

for the energy related issues. To our knowledge - this is a 

completely new approach. 

Uncertainty is handled identically as explained earlier. We 

can therefore present the new activity-based method for 

assessing costs and revenues, energy consumption and waste 

generation. 

5 . 0 Activity-Based Life-Cycle Assessment 

Method 

In Figure 1, the new Activity-Based Life-Cycle Assessment 

method (Activity-Based LCA) is presented. The new method is 

based on the ACU method found in (Emblemsvåg and Bras 

1994; Bras and Emblemsvåg 1996). Compared to the original 

method, changes have been made to capture energy and waste 

related issues. The method has also been changed to facilitate 

the design of models in a more systematic fashion. The most 

important changes are: 

1. Step 2 has been added to make the modeling easier. There are 

in general three fundamental types of resources: economical 

-, energy related - and waste related resources. Some 

resources can be combinations of at least two of these 

fundamental resources, like ‘material’ which is a) associated 

with costs of purchase (economical), b) it consumes energy 

when it is produced (energy related) and c) it generates waste 

when it is used (waste related). 

2. Step 3 has been generalized. Now we are not merely using 

the phrase ‘Cost Drivers’ but the phrase ‘Drivers’ which 

refers to cost- and/or energy- and/or waste drivers. We have 

also introduced the terms resource- and activity drivers (see 

Nomenclature). In Figure 2, a schematic illustration is 

given; the assessment object consumes activities (measured 

by activity drivers) which in turn triggers the usage of 

resources (measured by resource activities). By making 

these distinctions the modeling becomes far easier and 

changes can be made more easily later on if desired. With 

respct to consumption intensities, we distinguish between 

two types of consumption intensities; fixed and variable. A 

fixed consumption intensity is a consumption intensity that 

is fixed no matter what the magnitude of the drivers are (e.g. 

fuel price for Marine Gas Oil (MGO)), while a variable 

consumption intensity varies as the magnitude of the drivers 

vary (e.g. the machine hour price). 

3. Step 5 (Step 4 in the original ACU method) has undergone a 

similar generalization 


Step 7 - Iterate Steps 1 - 6 

No 

No 

Step 1 - Create an Activity Hierarchy and 

Activity Network 

Step 2 - Identify the Resources 

Step 3 - Identify and Order the Resource 

Drivers and Activity Drivers and 

Find the Consumption Intensities 

Step 4 - Identify the Relationships between 

Activity Drivers and Design Changes 

Step 5 - Find/Compute the Cost, the energy 

consumption and waste generation 

of the Consumption of Activities 

Step 6 - Is the Model satisfactory? 

Yes 

Step 8 - Is the Design satisfactory? 

Yes 

Distinguish: 

- Design Dependent Activity Drivers 

- Design Independent Activity Drivers 

Distinguish: 

- Select best design; no 

explicit relationships needed 

- Modify design, use: 

* Mathematical functions, or 

* Action charts 

Use commercially available 

MS Excel and Crystal Ball 

software 

Use profitability distributions, 

sensitivity analyses and 

trend charts 

Use profitability distributions, 

sensitivity analyses and 

trend charts 

Figure 1 - The Activity-Based Assessment Method for Usage in Life-Cycle Design. 

Resources 

Resource 

drivers 

Resource 

assignment 

Activities 

Activity 

drivers 

Activity 

assignment 

Assessment 

objects 

Figure 2 - The Usage of Activity - and Resource Drivers. Based on (Turney 1991). 

We have moved from just looking at profitability 

maximization to profitability maximization and/or 

minimization of energy and/or waste generation. 

4. Step 6 and 8 was one single step (i.e., Step 5) in the 

original ACU method. Checking the model is tedious and 

may include a total rework of the model, and the check 

should always be performed before using the model. 

Therefore, we have introduced two separate steps. 

6 . 0 Illustrative Example - Cost, Energy and 

Waste Assessments for an UT 705 Platform 

Supply Vessel 

In this section, we provide an example of how to apply our 

Activity-Based LCA method using an UT 705 Platform Supply 

Vessel (PSV) as a case study. This specific type of PSVs is 

designed by Ulstein International AS in Ulsteinvik, Norway. 

6 . 1 Supply Vessel Terminology 

In general, operating a platform supply vessel is not an 

easy task, because there are many shipping companies 

competing for the same contracts and there are many things that 

can go wrong. The longer the time-charter contract is, the 

harder the competition, because the spot-market contracts are on 

a short time basis and the revenues will therefore be associated 

with large uncertainties. Even though the revenues are stable 

on a time-charter contract, there are still problems to overcome, 

which are mostly related to the maintenance-, service- and repair 

activities, which are defined as follows: 


• Maintenance refers to all maintenance activities that can be 

done while the vessel is in service. 

• Repair is unplanned service. 

• Service is planned maintenance activities that require the 

vessel being out of service. This happens every time the 

vessel is docking. How often the vessel docks depends on 

the policy of the ship owner, but the vessel has to be 

docked every five years due to class specifications given by 

Det Norske Veritas (DNV). 

Two more important definitions are needed for this example: 

• Lay day: An amount of days - specified in the contract 

between ship owners and charterer - when necessary repair, 

service and maintenance can be done. 

• Off-hire: The vessel is not capable of fulfilling the contract 

- planned or not. In the contract between the ship owners 

and the charterer this is specified in detail. In this paper, 

planned services on dock (kept within the lay days) are not 

considered off-hire. 

And in this context, there are two key questions : 

1. How can we reduce the amount of off-hire? 

2. How can we reduce the Life-Span Costs, -Energy 

Consumption and -Waste Generation? 

Depending on the contract, off-hire will come into effect in 

different situations. For FAR Scandia which is owned by 

Farstad Shipping ASA the contract is specified as follows: 

• Planned service on dock is not considered off-hire. 

• Unplanned repairs are considered off-hire. 

• The ship owners are given one lay day per month, but the 

maximum annual aggregated number of lay days is 6 lay 

days per year. Hence, if the time for annual service exceeds 

6 days, the additional time is considered off-hire. 

6 . 1 Creating the Model 

We will now go through our Activity-Based LCA method 

step by step and illustrate how this example can be solved. 

Step 1 - Create an Activity Hierarchy and Network 

. We start with forming an activity hierarchy followed by an 

activity network. In Table 1, the activity hierarchy for the 

model is presented. This is not the only way of breaking down 

the life-span, but it is convenient for this model. Always 

choose activities about which good information can be found, 

provided that the chosen activities do capture the costs, energy 

consumption and waste generation well. 

Table 1 - Life-Span Activity Hierarchy for the UT 

705 Platform Supply Vessel. 

Level 1 Level 2 

Use Vessel A1 Load Vessel A11 

Be in Service A12 

Stand By A13 

Service Platform A14 

Be out of ServiceA15 

Certify Class A2 

Service Vessel A3 Maintain Vessel A31 

Service Vessel on Dock A32 

Repair Vessel A33 

As depicted in Table 1, two different levels of activities are 

present. For example, activity A3 (‘Service Vessel’) consists of 

three level 2 activities - ‘Maintain Vessel’, ‘Service Vessel on 

Dock’ and ‘Repair Vessel’. In the activity network (see Figure 

2), we use the lowest level activities from the activity hierarchy 

(the shaded cells in Table 1). There are no design decisions to 

be made in this (simple) model. 

A11 A12 A13 A14 A15 

A2 A31 A32 A33 

Figure 3 - Activity Network (Icons as in (Greenwood 

and Reeve 1992). 

Step 2 - Identify the Resources. We have four resource 

elements (see Nomenclature): 

1) Fuel - the platform supply vessel we studied consumed 

Marine Gas Oil (MGO). 

2) Overhead costs - the costs related to running the ship 

owner’s organization. 

3) Labor - the labor performed in the shipyard when the supply 

vessel is being serviced, maintained and repaired. 

4) Spare parts - the spare parts the supply vessel requires during 

service, maintenance and repair. 

Step 3 - Identify and Order all Necessary Activity 

and Resource Drivers and Find the Consumption 

Intensities. 

In the UT 705 model we utilize the following resource drivers: 

• Running Hours; This is a cost driver used to determine the 

use pattern of the vessel, and it plays a key role in 

determining when the vessel is off-hire. Furthermore, 

‘running hours’ is the cost driver by which the overhead is 

distributed. 

• Fuel Consumption; This is a cost-, energy- and waste driver 

that simply keeps track of the fuel costs, the energy 

consumption, and the waste generation for the vessel. 

• Labor Hours; This is a cost- and energy driver that keeps 

track of the labor costs and the energy consumption 

associated with servicing the vessel. 

• Number of Parts; This is a cost- and energy driver that 

keeps track of the parts costs, the energy consumption 

associated with consuming spare parts in the service 

activities. 

There is no need for activity drivers in this example because we 

are only studying a single product. To find the quantity of cost, 

energy and waste drivers, as well as their consumption 

intensities, historical data has been obtained by 

1. Asking the crew on FAR Scandia, Jan H. Farstad, Bjarne 

Nygaaren (Farstad Shipping ASA, Ålesund, Norway) and 

Jim Watt (Farstad Shipping Ltd., Aberdeen, Scotland) to 

fill out forms. 

2. Using invoices up to four years old from different 

shipyards. 

In Table 2, the historical data obtained from FAR Scandia is 

presented and the fuel consumption resource driver is quantified. 

However, in order to quantify the impact and cost over a full 

year, we need to scale the fuel consumption for a typical 

mission (which lasts 48,2 hours) up to a full year, which is set 

to be roughly 6500 running hours per year. 

In Table 3, the assumed chemical releases related to the fuel 

consumption driver and other relevant information needed to 

compute the Waste Index are presented. Most of the releases are 

due to the emissions from the machinery, except Tributyltin. 

Tributyltin is released as the self-polishing paint is worn off 

when the vessel is moving 


Table 2 - Typical Mission in 1995 - using FAR Scandia as Basis. 

Activity Mode of Usage 

Waste Elements 

Running 

hours 

Speed 

[nm/h] 

Fuel Consumption 

[1000 kg/day] 

A11 In port 9.3 0.0 1.0 

A13 Stand By 0.0 0.0 4.0 

A12 Economic Speed 2.1 10.0 14.3 

A12 Full Speed 15.3 14.0 21.4 

A14 Service Platform 21.5 0.0 4.8 

Table 3 - Input Information to the Fuel Consumption Waste Driver. 

Natural Amount 

[tonn] 

Release per Unit 

Waste Driver 

[tonn/year] 

Balance Time 

[year/tonn] 

C Waste Index 

[WU/tonn] 

Resource related: 

Fuel Waste Content 1.0 * 10 2 

Activity related: 

To atmosphere: 

CO 7.5 * 10 10 

7.7 2.0 * 10 -2 

95.2 % 2.0 * 10 -9 

CO2 6.0 * 10 11 

3.1 * 10 3 

1.5 * 10 2 

95.2 % 7.4 * 10 -4 

SO2 5.5 * 10 10 

9.6 5.0 * 10 -3 

95.2 % 8.3 * 10 -10 

To ocean: 

Tributyltin 5.0 * 10 5 

5.4 * 10 -4 

1.0 4.8 % 4.8 * 10 -8 

Fuel 3.1 * 10 3 

1.0 * 10 2 

Table 4 - UT 705 PSV Resource Drivers and Consumption Intensities. 

Activity Resource Drivers Cost C.I. Energy C.I. Waste Index 

A11 Fuel Consumption 113 tonn/year 190 $/tonn 4.65 MJ/tonn 100 WU/tonn 

Running Hours 2,705 hours/year 241 $/hour 0.00 MJ/hour 

A12 Fuel Consumption 2,292 tonn/year 190 $/tonn 4.65 MJ/tonn 100 WU/tonn 



Running Hours 0 hours/year 241 $/hour 0.00 MJ/hour 



A15 Off-hire Hours 8 hours/year 658 $/hour 0.00 MJ/hour 

A2 EO Class (Annual) 1.0 1,581 $ 

EO Class 0.2 6,303 $ 

IOPP Cert. (Annual) 1.0 682 $ 

IOPP Cert. (Interm.) 0.2 758 $ 

IOPP Certificate 0.2 985 $ 

A31 Labor Hours 3,500 hours/year 50 $/hour 0.01 MJ/hour 

Number of Parts 20 parts/year 1,000 $/part 10 MJ/part 

A32 Labor Hours 3,500 hours/year 50 $/hour 0.01 MJ/hour 


A33 Labor Hours 16 hours/year 50 $/hour 0.01 MJ/hour 


. Not all data was available and we have highlighted in bold 

those numbers which we had to guess. Waste elements have 

been split into resources and activities related waste elements. 

Resource related waste elements are wastes created when the 

resource (fuel in this case) is produced. For the fuel, the 

resource-related waste generation is all the waste per tonn fuel 

created from extraction through the oil refinery and sale. This 

number is not available so we simply guessed. Since the 

purpose of this example is to illustrate our method we deemed 

this acceptable. Activity related waste elements are created as the 

vessel is consuming the fuel. This is the waste most people 

will call pollution due to the use of the vessel. In Table 4, we 

have summarized all resource drivers, and cost, energy and waste 

consumption intensities (Cost C.I., Energy C.I. and Waste 

Index, respectively) for the UT 705 PSV example. 

Step 4 - Identify the Relationships between Cost 

Drivers and Design Changes. Because no design effort is 

defined, this step is not applicable for this model. 

Step 5 - Find and Minimize the Cost o f 

Consumption of Activities. For determining the cost, 

energy and waste of an activity, multiply each resource driver in 

the activity by its respective consumption intensity and sum the 

totals for the activity. For example, for calculating the cost of 

activity A11, multiply 113 tonn/year times 190 $/tonn, 


multiply 2,702 hours/year times 241 $/hour, then sum the 

resulting two numbers (21,470 $/year and 651,182 $/year, 

respectively), which results in a cost of 672,652 $/year. The 

energy consumption and waste generation are calculated in the 

same way. The same procedure is followed for each activity, 

and then the costs, energy consumption and waste generation are 

summed for all activities to get the total UT 705 supply vessel 

cost, energy consumption and waste generation for one year (see 

Sections 6.3.1, 6.3.2 and 6.3.3). 

To facilitate the calculations, we implemented the model 

using commercially software - MS Excel ® and Crystal Ball ® . 

The Crystal Ball ® software is employed to handle the 

uncertainty in the model. Detailed descriptions can be found in 

(Bras and Emblemsvåg 1996). The Crystal Ball® software 

allows user defined uncertainty distributions (in assumption 

cells) and finds the resulting uncertainty distributions (in user 

defined forecast cells) numerically by using a Monte Carlo 

simulation. There are two types of assumptions - user defined 

and user predefined. The user defined assumptions are modeled 

in the assumption cells (as in Figure 4), and these can be 

changed whenever the user desires. In the complete model there 

are many user defined assumptions. For reasons of brevity, we 

will not present these 

A11 Fuel Consumption [tonn/year] 

102 107 113 119 124 

Figure 4 - Modeling an User Defined 

Assumption Cell. 

The user predefined assumptions are assumptions that are 

made by the designer/assessor in order to simplify the modeling 

based on the user (customer) preferences and (modeling) budget. 

These assumptions are embodied in the model framework and 

consequently much harder to change. In this model, the 

following predefined assumptions are made: 

• The historical data is used as a good guideline for the future 

development. 

• The revenues and costs will follow regular inflation; thus, 

the real revenues and costs are assumed constant. 

With these assumptions embodied in the model, Monte Carlo 

simulations are made. A high number (10,000) of trials is used 

in the simulations to obtain a low mean standard error. To 

facilitate sensitivity analysis, we chose all uncertainty 

distributions to be triangular with ± 10% (as in Figure 4). The 

sensitivity analysis is performed by measuring how the 

variances of the different assumption cells affect the variances of 

the forecast cells. 

Step 6 - Is the Model Satisfactory? The model includes 

some numbers that had to be guessed, thus the model is not 

satisfactory when it comes to the credibility of the results. 

However, credibility in the results is not the purpose of this 

example - the focus is to illustrate the use of our method, and 

for this purpose the model is satisfactory. 

Step 7 - Iterate Steps 1 - 6. This is not necessary for the 

illustration of the method. 

Step 8 - Is the Design Satisfactory? As explained 

earlier in this paper, the focus is on the method and not the 

results. However, in a project with ‘Møre forsking’ in Ålesund, 

Norway, the complete costing model is presented for this 

supply vessel (Fet, Emblemsvåg et al. 1996). It includes 501 

assumption cells and 65 forecast cells and is roughly 800 kB in 

size compared to the simplified model presented here which is 

only 41 kB in size and has 56 assumption cells and three 

forecast cells. The ‘complete’ model, however, does not include 

the energy and waste generation assessments. 

6.3 Results 

In this section, we present the results as follows; a) cost 

and revenue, b) energy consumption, and c) waste generation. 

6.3.1 Cost and Revenue Related Results 

In Figure 5, the frequency chart for the Annual Life-Span 

Costs is presented. The frequency chart is a numerical 

approximation to the exact probability distribution, and in 

Figure 5 we see that the mean expected Annual Life-Span Cost 

10 000 Trials 

Forecast: Annual Life-Span Costs 

Frequency Chart 

63 Outliers 

.022 

.017 

.011 

.006 

.000 

3 075 000 3 143 750 3 212 500 

[$/year] 

3 281 250 3 350 000 

Figure 5 - The Annual Life-Span Costs for an UT 

705 Platform Supply Vessel. 

Finance Costs [$/year] .56 

Fuel Consumption Intensity [$/tonn] .45 

Crew Costs [$/year] .37 

A12 Fuel Consumption [tonn/year] .35 

A33 Number of Parts [-/year] .20 

A31 Labor Consumption Intensity [$/h] .15 

A32/A33 Labor Consumption Intensit... .14 

A32 Labor Hours [h/year] .13 



A33 Part Consumption Intensity [$/P... .07 


Insurance Costs [$/year] .06 


A32 Part Consumption Intensity [$/P... .03 

Sensitivity Chart 

Target Forecast: Annual Life-Span Costs 


223 

167 

111 

55.7 

-1 -0.5 0 0.5 1 

Measured by Rank Correlation 

Figure 6 - Sensitivity Chart for the Annual Life- 

Span Costs. 

0

is 3,212,500 $/year, and that it can range between 3,075,000 to 

3,350,000 $/year. The chart in Figure 5 can be used by the 

ship owner when he/she is negotiating with the charterers. 

The sensitivity chart in Figure 6 can be used by a ship 

owner to identify where and how he/she can reduce the 

operational costs caused by the daily work. In Figure 6, we see 

that reducing the fuel consumption is one effective way to 

reduce the operational costs. This can be done by tuning the 

machinery better, using different machinery and/or another fuel. 

In the project between ‘Møre forsking’ and Farstad Shipping 

different fuel options were investigated, but it it was found that 

the current fuel provides greater savings for the ship owners 

than the competing IF 40 heavy fuel oil. Interestingly, the 

most important cost contributor was found to be the finance 

costs and in 1995 Farstad Shipping undertook a refinancing of 

their entire fleet to reduce these costs. 

6.3.2 Energy Related Results 

10 000 Trials 6 Outliers 

Frequency Chart 

.024 

.018 

.012 

.006 

.000 

Forecast: Annual Life-Span Energy Consumption 

12 500 13 625 14 750 15 875 17 000 

[MJ/year] 

Figure 7 - The Annual Life-Span Energy 

Consumption for an UT 705 PSV. 

The energy part can be treated in a similar fashion as the cost 

part. In Figure 7, the frequency chart for the energy 

consumption is presented. The estimated mean is 14,750 

MJ/year. Unfortunately, this number does not tell us much on 

its own because we have no good frame of reference. However 

as more vessels are studied these numbers will be just as 

meaningful as the cost estimates. 

Just as the sensitivity chart in Figure 6 can be used to trace 

cost drivers, the sensitivity charts in Figure 8 and 9 can be used 

to trace energy drivers. From Figure 8, we learn that there are 

three large energy drivers: 

1. Fuel Energy Content - the energy spent in making the fuel 

from extraction through oil refining and sale. 

2. The usage of the vessel to and from the platform (activity 

A12) is the single most energy demanding activity. 

3. The usage of the vessel at the platform (activity A14). 

There are other energy drivers, but these are so small in 

comparison that we have to run the model without the three 

previously mentioned energy drivers to detect them. The 

sensitivity chart for this run is presented in Figure 9. As we 

can see in Figure 9, the energy drivers related to the usage of 

spare parts are (after the fuel related energy drivers) the next 

important area of energy consumption to focus on. 

Unfortunately, Farstad Shipping ASA cannot affect this energy 

consumption at all, because they have no information on which 

manufacturer uses the least amount of energy in production of 

spare parts. Because energy is such an important asset, future 

product data sheets should give information on the energy 

content, in our opinion. 

244 

183 

122 

61 

0 


Target Forecast: Annual Life-Span Energy Consumption 

Fuel Energy Content [MJ/tonn] .77 




-1 -0.5 0 0.5 1 



Span Energy Consumption. 

Target Forecast: Annual Life-Span Energy Consumption 


A31 Parts Energy Content [MJ/Parts] .33 


A32 Parts Energy Content [MJ/Parts] .12 

Energy Content Labor [MJ/h] .12 




-1 -0.5 0 0.5 1 



Span Energy Consumption. 

6.3.3 Waste Related Results 

Because of the partial lack of data the waste generation aspect of 

the model is the most unreliable, but we believe that we are 

able to illustrate the usage of the method. In Figure 10, we see 

that the mean expected waste creation is roughly 305,000 

WU/year. Because we used an entirely new way of assessing 

the creation of waste (namely, using a new Waste Index) this 

number currently does not tell much, but will become more 

meaningfull as waste generations for other products are 

estimated using the same approach. By comparing the waste 

generation estimate in Figure 10 to the waste generation 

estimate of another vessel, we could identify which vessel was 

more environmentally friendly and by how much. In this 

manner this simple index can serve well in design (except for 

designs where the given biological and health and safety 

restrictions mentioned earlier apply). 

Forecast: Annual Life-Span Waste Creation 

10 000 Trials Frequency Chart 32 Outliers 

.023 

.017 

.012 

.006 

.000 

260 000 282 500 305 000 

[WU/year] 

327 500 350 000 

Figure 10 - The Annual Life-Span Waste 

Creation for an UT 705 Platform Supply Vessel. 

In Figure 11, the sensitivity chart for the waste generation is 

presented. The consumption of fuel is the most important 


233 

174 

116 

58.2 

0

waste driver. As noted in Section 6.2, it is also the most 

significant energy driver. This means that by reducing the fuel 

consumption the overall environmental impact of the vessel can 

be reduced. 


Target Forecast: Annual Life-Span Waste Creation 

Fuel Waste Content [WU/tonn] .78 



-1 -0.5 0 0.5 1 



Span Waste Creation. 

Because the waste drivers in Figure 11 were so dominant, 

the model was run once more to find out which waste drivers 

were next important. The results are given in Figure 12 and we 

see that the CO2 releases are much more environmentally 

unfriendly than the releases of CO, SO2 and Tributyltin. In 

fact, the releases of CO, SO2 and Tributyltin did not pass the 

3% cut-off limit of the sensitivity chart. Is this a reasonable 

result? From a long run point of view it is clear that the 

increasing amount of CO2 in the atmosphere is probably more 

dangerous than the release of, say, Tributyltin. Tributyltin has 

negative effects for at least some biological entities; some 

spices of sea snails change sex as a result of too high 

Tributyltin concentration in their habitat. How this will affect 

the global environment we can only speculate. We see that our 

Waste Index is captures the general effect of the releases on our 

environment. 

Target Forecast: Annual Life-Span Waste Creation 

CO2 Released Amount [tonn/year] 1.00 


-1 -0.5 0 0.5 1 



Span Waste Creation. 

How about local effects? We see from the preceding that the 

global CO2 effect is considered to be much more dangerous than 

the effect of Tributyltin. However, we know that Tributyltin 

has a local effect on the environment. The Waste Index can also 

be used to compare the effects on local regions - it all depends 

on how we define the control volumes. For example, if we 

narrow the assessment to the North Sea Basin, the control 

volumes have been reduced dramatically compared to the entire 

earth. However, the release of Tributyltin is the same, thus the 

index will increase, thus pointing out the fact that Tributyltin 

can have local effects. 

We can also use the Waste Index to answer the question at 

what point becomes the release of Tributyltin unacceptable, that 

is, when does the concentration in the control volume reach the 

safety limits? If we use the safety limits in the San Francisco 

Bay area, which is 5.0 ppt (parts per trillion) in shallow water 

(Department of Pesticide Regulations 1996) and the released 

amount of Tributyltin from Table 3, then we find that we need a 

control volume of at least 1.08*1011 m3 per vessel to stay on 

the recommended side. Farstad Shipping ASA operates about 

20 vessels in the North Sea and is considered to be one of the 

largest companies in the business. Since the average depth of 

the North Sea Basin is roughly 50 meters, Farstad Shipping 

ASA alone needs to operate in an area greater than 43,200 km 2 

area to avoid causing a Tributyltin concentration higher than 5 

ppt. Since the area of the North Sea Basin is in the magnitude 

of 500,000 to 1,000,000 km 2 there should be no problems for 

Farstad Shipping ASA for its North Sea operations. However, 

if we further narrow down the control volume to some of the 

busy harbors in the world, we would exceed this 5.0 ppt limit. 

As demonstrated by the preceding, we can use the Waste 

Index for any geographical area. The index provides a 

comparison between different products and different releases 

caused by the different products. It is of course impossible to 

verify the index by this simple example alone, but we believe 

that this index is an improvement compared to many indices and 

indicators mainly because it takes into account what the natural 

system can handle (balance time) and how the natural system 

has set the ‘default’ ratios between the different substances (the 

ratio between released amount and natural amount). In other 

words, we simply accept that ‘nature knows best’, and our index 

estimates how far we are off ‘nature’s advice’. 

As an aside, it should be noted that we can draw some 

interesting analogies between economics and waste generation. 

Consider the following. Our monetary system is a relative 

system where the national banks (e.g. Bank of America) 

determine how many (e.g.) dollar bills are available and the 

market determines the exchange rate between these paper notes 

and real values (e.g. food, houses and cars etc.) based on the 

total number of available dollar bills and the demand for the real 

values. Our waste system could work in a similar manner. The 

society determines how much waste is generated (similar to the 

national banks) and an international database (similar to the 

market) is used to calculate the associated Waste Indices, based 

on scientific models (similar role as the exchange rate) of the 

environment. Just as our monetary system does not reflect true 

costs and revenues, such a waste system does not necessarily 

reflect true environmental impact. The reason is that the 

exchange rates will never be absolutely ‘correct’, but that does 

not matter as long as the framework in which the exchange rates 

are being set are somewhat stable over time. Similar to the 

health of a country’s economy, we can define the health of its 

(environmental) control volume. A strong economic country 

will have a relatively strong currency compared to other 

countries, while a clean control volume will have a low Waste 

Index compared to unhealthy control volumes. Thus, the goal 

is a low Waste Index and a high monetary value. 

7.0 SUMMARY AND FUTURE WORK 

In this paper, we have presented and illustrated the use of a 

comprehensive new method for cost -, energy - and waste 

assessments and tracing for use in Life-Cycle Design. The 

method is based on our previous Activity-Based Costing with 

uncertainty method. The incorporation of energy - and waste 

assessments is quite straightforward as we have energy- and 

waste drivers to capture energy- and waste related issues, 

respectively, just like we have cost drivers for the costing part 

of the method. The major challenge is to identify a single unit 

index/indicator for the waste part of the method and we have 

defined a new Waste Index for this purpose. 

We stress that the Waste Index presented in this paper is just 

a prototype and we have to carry out many case studies to 

investigate its behavior and identify improvements. We know 

some of its weaknesses and advantages. Its weaknesses are: 

• It cannot handle products which are associated with 

biological processes or have biological components. This is 


evident from the fact that biological entities reproduce and 

change. 

• The index is not capable of tracking health risks like 

mustard gas which are very lethal to humans do not affect 

the environment much in the long run. We therefore need to 

adjust the index to incorporate safety regulations. An 

associated issue to be resolved is the handling of highly 

concentrated physical releases (e.g., dust). 

• The different parameters currently work as step functions, 

but in the real life decay happens gradually. The most 

common model is the first order decay model which we are 

currently investigating. The advantage of using step 

functions is their simplicity and conservative behavior. The 

disadvantage is that different decay rates/processes (e.g., first 

order exponential) are treated equally. 

Its advantages, however, are: 

• It allows the usage of one single index number that blends 

easily into the activity-based method. In fact any 

index/indicator can be used, but the existing indices and 

indicators have major shortcomings by not considering the 

thermodynamic and chemical relations. 

• It incorporates thermodynamic and chemical reactions and 

how fast these reactions occur. As said, this is missing in 

most indices and indicators. 

• It can be used at any scale of implementation from local to 

global by adjusting the control volumes. 

• It is not political - taking away or at least reducing the 

potential for discussions whether a specific release affects, 

say the green house effect, in this or that manner. 

To improve the Waste Index we are considering the usage of 

threshold values for sustaining the biological diversity in the 

control volume because preserving the biological diversity is 

important for humans both directly and indirectly via the usage 

of biological entities. In a similar fashion, the threshold for 

human health risks will be incorporated. The question now is, 

how do we manipulate these three ratios into one single waste 

index? The answer is unclear at the present, but we believe that 

we are on the right track with the posed Waste Index. 

No matter what index we use, there are several advantages 

to using an activity-based method along the lines of the 

presented method: 

• All the different contributors of costs, revenues, energy and 

waste are traced in one single method. A designer, 

company, etc. has to learn only one method and not three 

different methods (one method for each of the three areas). 

• Costs and revenues, energy consumption, and waste 

creation per unit product are assessed in an accurate manner. 

• It allows the modeling and handling of uncertainty. 

• Overhead energy consumption and overhead waste creation 

can be handled. Such overheads may contribute significant 

amounts to the overall energy consumption and waste 

creation. 

Although we used a relatively simple supply vessel example, 

the method provided insight in the mean life-cycle cost/revenue, 

energy consumption, and waste generation, as well as their 

range. Furthermore, the sensitivity analyses provided insight 

identifying areas for improvement. In the example, the 

reduction of fuel consumption clearly results in win-win 

situations from a cost, energy, and waste point of view, as 

pointed out by the results. In our opinion, the capability of 

identifying such win-win situations is one of the most 

significant advantages of our integrated Activity-Based Life- 

Cycle Assessment approach. 

ACKNOWLEDGMENTS 

The example is supported by Farstad Shipping ASA and Møre 

Research in Ålesund, Norway. We gratefully acknowledge the 

support of NSF Grant DMI-9624787, the Norwegian Research 

Council and U.S.-Norway Fulbright Foundation for Educational 

Exchange. 

REFERENCES 

Ayres, R. U. (1995). “Life cycle analysis: A critique.” 

Resources, conservation and recycling (14): 199 - 223. 

Bras, B. and J. Emblemsvåg (1996). Designing For The 

Life-Cycle: Activity-Based Costing and Uncertainty. Design for 

X . G. Q. Huang. London, Chapman & Hall: 398-423. 

Brinker, B. J. (1997). Handbook of Cost Management . 

Boston, MA, Warren, Gorham & Lamont. 

Brooks, P. L., L. J. Davidson, et al. (1993). 

“Environmental compliance: You better know your ABC's.” 

Occupational Hazards (February): 41-46. 

Cooper, R. (1990). “ABC: A Need, Not an Option.” 

Accountancy (September): 86-88. 

Department of Pesticide Regulations (1996). Copper- and 

Tributyltin-Containing Pesticides , DPR Regulation No. 96- 

001. 

Emblemsvåg, J. and B. A. Bras (1994). Activity-Based 

Costing in Design for Product Retirement . Proceedings 1994 

ASME Advances in Design Automation Conference, DE-Vol. 

69-2, Minneapolis, American Society of Mechanical Engineers: 

351-362. 

Fet, A. M., J. Emblemsvåg, et al. (1996). Environmental 

Impacts and Activity Based Costing during Operation of a 

Platform Supply Vessel. Ålesund, Norway, Møreforsking. 

Fowler, R. J. (1990). International Policy Responses to the 

Greenhouse Effect and their Implications for Energy Policy in 

Australia. Greenhouse and Energy . D. J. Swaine. Melbourne, 

C.S.I.R.O. 

Greenwood, T. G. and J. M. Reeve (1992). “Activity Based 

Cost Management for Continuous Improvement: A Process 

Design Framework.” Journal of Cost Management for the 

Manufacturing Industry 5(No. 4 Winter): 22-40. 

Keoleian, G. A. and D. Menerey (1994). “Sustainable 

Development by Design: Review of Life Cycle Design and 

Related Approaches.” Air & Waste 44(May): 644-668. 

O'Guin, M. (1990). “Focus The Factory With Activity- 

Based Costing.” Management Accounting (February): 36-41. 

Olsson, L. E. (1994). “Energy-Meteorology: A new 

Discipline.” Renewable Energy 5 Part II: 1243 - 1246. 

Raffish, N. and P. B. B. Turney (1991). “Glossary of 

Activity-Based Management.” Journal of Cost Management for 

the Manufacturing Industry 5(No. 3 Fall). 

Turney, P. B. B. (1991). Common Cents: The ABC 

Performance Breakthrough - How To Succeed With Activity- 

Based Costing . Hillboro, Or., Cost Technology. 

Turney, P. B. B. (1991). “How Activity-Based Costing 

Helps Reduce Cost.” Journal of Cost Management for the 

Manufacturing Industry 4(No. 4 Winter): 29-35.

An Activity-Based Life-Cycle Assessment Method - the Systems ...

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?