UCD SCHOOL OF BIOSYSTEMS ENGINEERING

Life Cycle Assessment of a Small Wind Farm

BSEN 40440 – Life Cycle Applications

Luke Martin

5/26/2015

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Contents Goal and Scope ....................................................................................................................................... 3

Goal ..................................................................................................................................................... 3

Renewable Energy System to be studied ............................................................................................ 4

Function of the system ....................................................................................................................... 4

Functional Unit .................................................................................................................................... 5

System Boundary ................................................................................................................................ 5

Allocation procedure .......................................................................................................................... 6

LCIA methodology and impacts .......................................................................................................... 6

Interpretation to be used.................................................................................................................... 6

Data Requirements ............................................................................................................................. 8

Assumptions ........................................................................................................................................ 9

Value Choices ...................................................................................................................................... 9

Limitations .......................................................................................................................................... 9

Data Quality Requirements ............................................................................................................... 10

Type of Critical Review ...................................................................................................................... 10

Type and format of the report .......................................................................................................... 11

Life Cycle Inventory ............................................................................................................................... 12

Data Collection .................................................................................................................................. 12

Data Calculation ................................................................................................................................ 14

Validation of data .............................................................................................................................. 16

Relating data to the function unit ..................................................................................................... 17

Refining the system boundary .......................................................................................................... 17

Allocation .......................................................................................................................................... 17

Life Cycle Impact Assessment ............................................................................................................... 18

General .............................................................................................................................................. 18

Selection of impact categories, category indicators and characterization models .......................... 18

Assignment of LCI results to selected impact categories ................................................................. 20

Calculation of category indicator results: ......................................................................................... 20

Resulting data after characterization: .............................................................................................. 21

Normalisation.................................................................................................................................... 22

Grouping ........................................................................................................................................... 22

Data Quality Analysis ........................................................................................................................ 22

Life Cycle Interpretation ....................................................................................................................... 24

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Significant Issues ............................................................................................................................... 24

Evaluation ......................................................................................................................................... 28

Conclusions ....................................................................................................................................... 29

Limitations and Recommendations .................................................................................................. 29

Critical Review ....................................................................................................................................... 31

Appendices ............................................................................................................................................ 32

Appendix 1 - Raw material extraction and manufacturing ............................................................... 32

Appendix 2 – Turbine assembly and deconstruction ........................................................................ 34

Appendix 3 – Turbine maintenance and use phase .......................................................................... 35

Reference List ........................................................................................................................................ 36

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Life Cycle Assessment of a Wind Turbine

Goal and Scope The wind energy sector is Ireland’s strongest growing renewable energy sector with 222

wind farms located on the island (IWEA, 2015). The popular adoption of this technology is

largely due to the pursuit of low-carbon intensive energy forms and to ease reliance on

fossil fuels (Martinez et al, 2008).

“Sustainable energy is to provide the energy that meets the needs of the present without

compromising the ability of future generations to meet their needs” (Ghenai, 2012). Based

on the premise of utilising the kinetic energy of the wind to generate a clean form of

electricity, wind power appears to be the ideal solution to this issue. However the

technology has recently come under scrutiny due to questions raised about wind power’s

relative sustainability when manufacture, transport and disposal processes are taken into

account (Tremeac & Meanier, 2009). Considering that these turbines are made from a

combination of metals, concrete and fibreglass, a considerable amount of energy derived

from fossil fuels is required during these stages of its lifecycle.

Life cycle analysis is a tool which can be utilized to determine the environmental impacts of

all of the stages of a renewable energy system life span and facilitates a more accurate

comparison of a RES with a conventional energy system. This study intends to apply this tool

to a wind turbine based on data sought from a wind farm in north county Dublin and assess

the emissions associated with its life cycle.

Goal

As stated in the introduction, a life cycle analysis is being carried out on a wind farm in Lusk

Co. Dublin. This site was chosen for the sake of simplicity; the farm consists of one Enercon

E-48 turbine and has a primary energy demand in the form of a food packaging plant.

This study aims to determine three things;

1. Are there any stages in particular within the turbine’s life cycle which has

considerable impacts on the environment?

2. How does a wind turbine’s associated environmental impacts compare to

conventional grid electricity use?

3. When all aspects of this wind turbine’s life cycle are quantified and converted into

the respective impact categories does it warrant the label of a “sustainable

technology”?

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The results are intended for an audience who are adept in life cycle analysis and have a

good understanding of the wind energy industry. The results are not intended to be used for

comparative assertion given the author’s lack of experience in the area of LCA however, the

results derived from this study may be compared to those in the literature in order to assess

their accuracy.

Renewable Energy System to be studied

In order to assess the true sustainability of wind energy

a site in Lusk Co. Dublin was selected for this study for

two main reasons;

The wind farm consists of only one turbine,

making the gathering of activity data and the

scaling of the model easier for the inexperienced

LCA practitioner.

A certain degree of familiarity is associated

between the author and this site given that the

wind mill can be seen from his house.

This site is owned by a company called “Country Crest” a

commercial farming company which recently expanded

its business to include packaged and processed foods. The

electricity generated from the on-site turbine is

predominantly used to power these processes. When the wind is not blowing at sufficient

speeds, the factory is powered by the Irish grid.

By enlisting in the help of GaBi, it is expected that a cradle-to-grave analysis will be

successfully executed. This expectation is made because a considerable amount of time will

be saved by the use of this software seen as it does all the calculations for the user. Hence

the study considers all stages of development of an “Enercon E48” turbine. Figure.2 has

segregated the system into several phases including raw material extraction, truck and ship

transport, component manufacture, turbine assembly, use phase, maintenance phase,

decommissioning and final disposal of turbine parts. The only exception is the recycling

phase; the reason for this phase’s omission will be discussed in the “limitations” section.

Function of the system

The function of the system is to convert kinetic energy derived from wind into rotational

kinetic energy in a turbine and subsequently into usable electricity to power on-site

functions. This is depicted in the following equation albeit a simplified version:

𝑃𝑤 = 1

2∗ 𝑝𝐴𝑉³

Where Pw= Wind Power

Figure 1: Enercon E-48 turbine (Country Crest.ie, 2015)

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p = Air Density A = Swept Area (m²) V = Wind Speed (m/s)

Obviously the wind does not blow at the same speed constantly and each turbine model has

a unique range rotor efficiencies at different wind speeds known as the power coefficient.

The model will include a number of scenarios which manipulate the inputs of this equation

along with the power coefficient, with data derived from a Weibull wind frequency

distribution specific to the site, in order to demonstrate the wind turbine’s effect on

emissions by offsetting the use of the Irish grid.

Functional Unit

The functional unit chosen for this study is the production of one MW of electricity. The

amount of goods packaged during the use phase has been manipulated to demand this

amount of power. All material quantities have been scaled to this unit.

System Boundary

Figure.2 depicts the system boundary in green. This study begins with raw material

extraction depicted as “metal” and “other material” extraction phases. This step can rely on

pre-existing processes in the GaBi database. The raw materials emerge from their respective

extraction phases as refined material ready to be manipulated into the desired parts. The

next step involves transporting these refined materials to either the turbine manufacturer in

Picardie, France or, in the case of concrete, directly to the assembly site in Dublin, Ireland.

Transported materials are processed into the respective turbine components via a number

of industrial processes such as casting, forging or stamping (Ghenai, 2012), summarised in

the “manufacturing phase”. Following this phase, the finished wind mill parts are exported

via truck and ship transport from Picardie to Dublin where the “assembly phase” takes place

involving a crane process, an excavator process and a bolting/drilling process to build the

wind mill.

Adjacent to this phase is the “use phase”, shaded in green in figure.2. In scenarios with

optimum wind conditions and frequencies (≈6-12 m/s) wind power can completely satisfy

the 1MW power demand however any scenario outside this range requires the use of the

Irish grid. The “Irish Grid” process is a pre-existing process within GaBi which is more than

acceptable to use in this project as outlined in the life cycle inventory.

The end-of-life phases are split into three phases in figure.3. The “decommissioning phase”

involve the dismantling and sorting of turbine components into the respective raw materials

of steel, iron, aluminium, copper, PVC and glass fibre. Following the methods of Tremeac &

Meanier (2009), concrete is assumed to be covered over in top soil and left in the ground

hence a landfill of concrete process should adequately represent the end-of-life activity of

this material.

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While accurate activity data on the proportion of each of these materials going to landfill or

recovery was secured, there was an issue with how the model dealt with recycling (depicted

by broken green lines). This phase had to be omitted from the original system boundary due

to time constraints. This point is further elaborated on in the “limitations section”.

Allocation procedure

According to Martinez et al (2008), allocation is not a major issue in wind turbine LCA’s and

any possible impacts on final results are minimal; hence all processes within the system

boundary are assumed to have only one function to avoid this issue.

LCIA methodology and impacts

The first two objectives of this study; the sustainability of a wind turbine and how it

compares to the Irish grid will be assessed on their impacts towards global warming

potential (GWP), resource depletion and water use. The third objective which investigates

any processes with a particularly high impact to the environment may consider other

impacts such as acidification or eutrophication impacts.

CML 2001 was chosen as the characterisation method for this project based on the premise

that it uses midpoints over endpoints. Endpoint impact information is considered more

useful to policy-makers, especially those without a scientific background, as it expresses

impacts in a form that is easier understood. For example Eco-indicator 99 expresses the

impact of acidification processes as the amount of species extinct per m² per year.

According to Bare et al (2000), the majority of LCA experts believe that extending impact

categories as far as end point reduces the integrity of results because the availability of

reliable data remains too limited. Considering the target audience of this report are adept in

LCA and wind energy, it can be reasonably assumed that they have scientific backgrounds

hence mid-point indicators are used to express process emissions in this study.

While GaBi conveniently affords the user the opportunity of using multiple characterisation

methods the CML 2001 method is considered to be particularly reliable in its

characterisation methods especially with respect to European datasets.

Interpretation to be used

Following ISO14044:2006 standards; the interpretation will involve an analysis of the LCI

and LCIA results in order to identify the key contributors within the wind turbine’s life cycle

to the aforementioned impact categories. The interpretation will also assess the integrity of

the methods used to obtain these results and will consider the potential drawbacks with the

software, sampling errors and data quality.

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Concrete Iron Copper

Steel Aluminium Concrete Glass Fibre

Nacelle Rotor Substructure

Inverter Cables

Wind Mill

Maintenance

Metal Extraction Phase Other Material Extraction Phase

Copper Iron PVC

Manufacturing Phase

Truck Transport

Assembly Phase

Site

Infrastructure

Truck and Ship Transport

Wind Energy

Irish Grid

Energy

Food Packaging

(1MW demand)

Tower

Use Phase

(Including

upstream

processes)

Decommissioning Phase

Steel Aluminium Glass Fibre

PVC

Truck Transport

Recycling Phase

Landfill Phase

Credit for

Plastic/Metal

Recovery

Plastic/Metal/

Concrete to Landfill

Waste Flow

Mass Flow

Emission Flow

Refined System Boundary

Power Flow

Figure 2: System boundary of the “Country Crest” wind farm, Lusk Co.Dublin. Original System Boundary

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Data Requirements

For data relating to raw materials, electricity generation and waste processes, pre-existing

processes from the GaBi database are acceptable. Seeking specific data on these aspects of

the model are not likely to have a significant impact on the overall environmental emissions

of the system. For example the GaBi database contains cradle-to-gate processes for all

respective raw material extraction and refinement phases of the model. These pre-existing

processes rely on data gained from global averages. This is acceptable at this point in the

model as this phase is so far removed from the conversion technology itself.

In a similar vein, the use of pre-existing processes are generally favoured over user-created

ones for the majority of the manufacturing phase. There are a lot of emissions associated

with these processes and the pre-existing processes are likely to represent the process in

reality than a user created one, especially a user lacking experience in specific industry

techniques such as casting or forging. There are a few occasions when either a user-created

process or an edited existing process can be included in the model. Bolting and drilling for

example, are assumed to have emissions associated primarily with the electricity they use

hence this process will require little activity data, reducing the likelihood of error.

In some cases, pre-existing processes are present which are based on regional averages. The

French, German and Irish grids are all present in the GaBi database for example; and these

processes rely on grid mix data from November 2014. Given the up-to-date accuracy of

these electricity processes, the pre-existing “Irish Grid” process was selected to power site

process in the absence of ideal wind speed as shown in figure.2. This process will have a

significant impact on the results of the model.

High precision, site-specific data is required for the wind-mill’s size specifications as such

data will have a direct impact on the turbine’s ability to generate electricity. This data will be

sought from the Enercon website. In addition, wind speed data for the site must be of high

temporal and geographical accuracy as this parameter will also have a profound effect on

the model.

As outlined in assumptions, the most direct route is always chosen when compiling distance

data. This will be calculated using the distance function in google maps. Pre-existing GaBi

transport processes will used to represent material and goods transport while exact

distances will be inserted into the model using parameters within these processes.

Finally, specific data will be utilised within the end-of-life stages of the model to determine

the proportions of material going to landfill or recycling. Pre-existing landfill and recycling

processes will be applied when possible. In the event when a material-specific landfill

process is not available, an existing landfill/recycling process will be manipulated to accept

the material in question.

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Assumptions

The largest assumption made in this model is that all power generated from wind is utilized

by on-site processes. In reality, the turbine provides power for approximately 150 local

households during optimum wind conditions. Since the main objectives of the study are

focused on the renewable energy system itself, this assumption is acceptable as these

households are well and truly outside the system boundary.

For raw material and component transport, it is assumed that the most direct route

suggested by Google maps is taken. In addition it is assumed the port nearest the terrestrial

site (Dublin) is used for component import.

Waste generated in the form of scrap during the “manufacturing phase” is assumed to be

100% recycled as the waste is generated on site and could theoretically be used in

subsequent component manufacture. (e.g. the “steel bending and stamping” process has a

5% scrap parameter attached to it)

Value Choices

This study is concerned primarily with GHG emissions, water usage and energy usage. In the

event that a process is identified with a formidable effect on another impact category such

as acidification potential, that impact category will also be discussed in the interpretation

phase. However upon making suggestions on how a process could minimize the turbine’s

overall environmental impact, the first three impact categories will override the latter.

Limitations

Design and development of the wind turbine will be omitted from the study due the fact

that this process itself does not make a significant physical contribution to emissions of this

now mass produced technology in comparison to the other phases in the life cycle (Rebitzer

et al, 2004).

Specific data for the quantity of materials used in an Enercon E-48 turbine could not be

located. The material quantities per MW of a wind turbine with a steel tower were taken

from Wilburn (2011) to overcome this limitation. The main drawback with this substitution

is that this data is based on American turbines however it offered the most precise data on

the quantities of materials scaled to 1 MW hence was ideal for this study.

Picking up on the omission of the recycling phase from the system boundary; the user did

not anticipate how GaBi dealt with the recycling of materials. The omission of this process is

not ideal as it does not come under the cut-off criteria of the model. A considerable amount

of material was recyclable hence the recycling phase was likely to reduce the overall

emissions associated with the wind turbine manufacture stage significantly. The pre-existing

“credit for recycling” processes within the database only function when linked to the

manufacturing phase earlier on in the model. This model used a hierarchical structure to

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encapsulate the renewable energy system which did not allow the recycling processes,

located in the disposal phase, to link up with the manufacturing phase upstream.

In order to overcome this issue, the entire model would have to have been redesigned. This

was not possible due to time constraints, with the model deadline fast approaching. As a

slight consolation, the recyclable portion of the waste was successfully diverted from landfill

within the model. As a result, the contribution of the disposal phase to life cycle emissions

has been muted slightly hence the results are still considered somewhat robust and should

allow for the objectives of this project to be achieved albeit with higher uncertainty.

Finally, the model is being created on an educational version of GaBi. This version has a

limited number of unit and system processes available to the user. This will require the

input of data gathered from industry and the literature. This information can be difficult to

locate hence there are likely to be gaps in the model due to this.

Data Quality Requirements

Close to the energy conversion source, data is expected to be up-to-date, geographically

relevant, technologically precise and relatively complete. Ideally manufacturer specific date

will be required in and around the use phase.

Background processes need not be as site specific. There are hundreds of LCA studies on

wind turbines hence any datasets obtained from the respective LCA databases can be

considered relatively robust. Hence In the event where data for a specific unit process or

raw material cannot be obtained, LCA data for a similar process or material will suffice as

opposed to omitting the parameter altogether. For example, for disposal and raw material

phases it is acceptable to use non-specific data from a similar study as the results are

unlikely to deviate significantly from results derived from site specific data.

Failing to find a suitable substitute process (i.e. in the case of recycling), the system

boundary will be altered to avoid the process’s inclusion. While the specific data in this

circumstance is available, it is difficult to determine a way to incorporate it into the model

hence a zero value is technically assigned. As highlighted in the limitations section, this zero

value is significant in the sense that it contributes to the muting of landfill values. It is clear

that if recycling had of been successfully included in the model, emissions from

manufacturing should also have been muted slightly.

Type of Critical Review

The critical review will assess whether the results and interpretation of the LCA satisfied the

goal and scope outlined by the author, whether there are any discrepancies or omissions in

the data or whether the author gave a well-rounded view of the subject. Ideally the project

should be reviewed by another LCA practitioner to ensure the absence of bias and personal

errors one might not be aware of.

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Type and format of the report

The format of the report will strictly adhere to ISO 14040 standards including a goal and

scope; inventory analysis, impact assessment and interpretation and will be written to cater

for an audience with a solid grounding in LCA and wind energy.

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Life Cycle Inventory

Data Collection

Raw Material Inputs Quantities of raw materials required per MW of electricity are acquired from Wilburn

(2011). Table.1 shows the exact quantities of steel, concrete, iron, fibreglass, copper and

plastic.

Table 1: Raw Material Quantities (Wilburn, 2011)

Material Proportion of turbine Mass per FU (kg/MW)

Stainless Steel 20% 116,800

Concrete 71% 402,000

Cast Iron 4.4% 24,925

Fibreglass 1.9% 10,780

Aluminium 1.4% 8,100

Copper 0.5% 2,800

PVC 0.08% 500

Transport Table.2 shows the distances the various parts required for turbine assembly need to travel

from the respective material merchants to the Enercon manufacturing facility in Picardie.

The parts are assumed to be sourced from the merchant closest to the Enercon facility

according to google maps. Within the model, these parts are represented by system

processes hence all upstream transport emissions from extraction source to processing

facility have been estimated and are included within the dataset. Parameter explorer will be

utilized to investigate the significance of increasing these transport distances.

Table 2: Refined material transport (google maps, 2015)

Material Truck Transport per FU (km)

Stainless Steel 184

Concrete 65

Cast Iron 184

Fibreglass 71

Aluminium 81

Copper 77

PVC 100

Table.3 displays the distances the turbine components are required to travel from Picardie

to the assembly site in Lusk, Co. Dublin. Again, google maps are used to estimate distances

and it is assumed that the most direct route was taken.

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Table 3: Distances of component transport from manufacturing plant to assembly site

Turbine Component Truck [France and Ireland] (km)

Ship (km)

Tower 442 880

Nacelle 442 880

Rotor 442 880

Inverter 442 880

Cable 442 880

Concrete 100 0

End-of-life transport is summarised in table.4. Material for landfill is assumed to be

exported to the nearby landfill at Balleally, Lusk while recyclable material is assumed to go

to a sorting centre in Malahide, Co. Dublin.

Table 4: End-of-life transport

Waste Treatment Method Distance (km)

Landfill 3

Recycling 15

Energy Inputs Industry averages derived from the GaBi database or from online sources are sufficient for

the use of electrical and thermal energy in processes such as welding, casting and forging

are utilised in this LCA. Table 5 shows the total energy required for each manufacturing

stage along with the conversion process used in real life and the process used to mimic this

in GaBi.

Table 5: Energy use by model processes

Component Manufacturing Process

GaBi Process Energy required (MJ)

Tower Forging, Rolling Steel Bending 13703 Electrical Nacelle Forging, Rolling Steel Bending, Cast

Iron System Process 54342 Electrical

Rotor Composite Forming Welding, Cast Iron System Process

17600 Electrical

Inverter Forging, Rolling Copper bending, Aluminium die cast

28285 Electrical 15390 Thermal

Cable Polymer extrusion Rod formation/ Assembly (us-o)

1440 Electrical 1264 Thermal

Concrete Construction System Process (Diesel; Covered in Transport)

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Emissions to air, water, soil GaBi is highly depended on for this aspect of the model. The pre-existing system and unit

processes within the database have associated emissions embodied within them. When a

user-process is created the associated emissions have to be input individually into the

process. While every effort was taken to ensure the accurate location and input of these

emissions, this inevitably leads to uncertainty due to the scant availability of such data on

the internet. Whenever possible, the practice of copying and editing an existing unit process

within GaBi is preferred to creating a new one provided it is at least similar in some way to

the process in real life.

Waste Table.6 shows the proportion of each material which is available for recovery or destined for

landfill. These proportions are taken from Martinez et al (2008) as precise activity data for

this particular site was not readily available.

Table 6: Proportion of materials for waste or recovery

Material Recycling (%) Landfill (%)

Stainless Steel 97 3 Concrete 0 100 Cast Iron 95 5 Fibreglass 48 52 Aluminium 35 65 Copper 28 72 PVC 72 28

Data Calculation

The main data calculations associated with this model are related to the “use phase”. The

function of this phase is to demonstrate the environmental effect of displacing fossil fuel

electricity generation with wind energy. In this model, a pre-existing system process of the

Irish grid mix is selected as the alternative power source to wind at this site. This process

consists of electricity generation from a combination of natural gas, peat, coal and a small

proportion of wind. When the wind is blowing at a velocity of 9 or 10 m/s, the turbine

operates at its highest efficiency and can completely satisfy the on-site energy demands.

The wind however, is a highly variable resource and does not blow consistently at these

optimum speeds. Table.7 shows the three main variables which determine the power

output of a wind turbine; wind speed, power coefficient and wind hours. The power

coefficient data, unique to this E-48 turbine and was acquired from Enercon (2012).

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Table 7: Weibull Wind Speed Distribution

Wind Speed Power Coefficient Weibull Wind Hours

m/s Cp hr

0 0 0 0

1 0 0.039992 350.32992

2 0 0.075234 659.04984

3 0.17 0.101903 892.67028

4 0.35 0.117783 1031.77908

5 0.43 0.122525 1073.319

6 0.46 0.117466 1029.00216

7 0.47 0.105108 920.74608

8 0.48 0.088447 774.79572

9 0.5 0.070333 616.11708

10 0.5 0.05303 464.5428

11 0.45 0.038 332.88

12 0.39 0.025925 227.103

13 0.32 0.016862 147.71112

14 0.27 0.010466 91.68216

15 0.22 0.006205 54.3558

16 0.18 0.003515 30.7914

17 0.15 0.001905 16.6878

18 0.13 0.000987 8.64612

19 0.11 0.00049 4.2924

20 0.09 0.000233 2.04108

21 0.08 0.000106 0.92856

22 0.07 4.16E-05 0.364416

23 0.06 1.92E-05 0.168192

24 0.05 7.96E-06 0.0697296

25 0.05 2.95E-06 0.025842

The wind-hours data was calculated using hourly wind frequency data from the Dublin

Airport weather station provided by Met Eireann (2015). The key to achieving the second

objective of this project is to incorporate this data into the model. This was facilitated with

the use of parameter explorer in which a number of wind speed scenarios were created to

include each of the rows in table.6. Figure.7 illustrates an “electricity chooser” process

which switches grid supply on and off pending on wind conditions. The current wind

scenario in this figure is set to 10 m/s; hence the grid is making no contribution to the 3.6

MJ (1MW) of power demanded by the “site processes”.

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Figure 3: Wind energy vs grid energy to satisfy energy demand

Validation of data

Table.8 shows a compilation of consistency checks carried out within each plan in the

model. There is one major outlier which will be discussed in the interpretation.

Table 8: Mass Balance Consistency Check

Process Mass In (kg) Mass Out (kg) Difference (%)

Tower Manufacture 74800 74700 -0.13368984

Nacelle Manufacture 35000 35000 0

Rotor Manufacture 27700 27700 0

Inverter Manufacture 9600 9600 0

Cable Manufacture 3250 3250 0

Site Infrastructure 402000 452000 12.43781095

Site Maintenance 13650 13650 0

Electricity Generation 566000 566000 0

Turbine Decommission 566000 566000 0

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Relating data to the function unit

All material quantities and by extension all energy inputs have been scaled to the functional

unit of one MW of electricity produced by the tactical collection of activity data as outlined

in table.6. The entire model is scaled to the “site processes” unit process in the use phase

which creates a demand for 1 MW of electricity. Obviously the wind turbine only generates

electricity when the wind is blowing at sufficient speeds so this factor is covered by

inputting the Weibull distribution specific to this site (Table.7) as a parameter in the model.

When the wind is not blowing at sufficient speed to satisfy the demand, power from the grid

makes up the difference as shown in appendix.3. The emissions associated with the Irish

grid have been modelled in order to demonstrate how these emissions are offset by wind

energy at this site.

Refining the system boundary

As stated in the “limitations” section it was necessary to refine the system boundary to

exclude the recycling phase of the model as outlined in figure.2. This was not down to a lack

of available activity data, as is usually the case when refining a system boundary but down

to an oversight made by the inexperienced user when designing the model. Due to time

constraints it was not possible to rectify the model to include recycling. As previously stated,

this will have a significant impact on the results however it does not render them useless.

Allocation

Allocation was avoided in two circumstances within the model by using two separate

techniques.

1. Waste flows associated with scrap of various metals during the “manufacturing

phase” were assumed to be 100% recycled. Despite this being an incorrect

assumption to make, the effect that this assumption will have on the model is well

below the cut-off criteria of the model; hence it will not have a significant effect on

the overall results.

2. During the “disposal phase” allocation was avoided by system expansion. Here a

“sorting” process was created which assigned exact proportions of waste materials

to their respective end-of-life process based on activity data gained from Martinez et

al (2008).

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Life Cycle Impact Assessment

General

This phase of the LCA, which assigns characterisation factors to the data compiled during

the inventory phase, will be carried out by GaBi. While the program offers a variety of

options to the user for completing this step it is necessary to select an appropriate

characterisation method, taking the scope of the project into account.

Selection of impact categories, category indicators and characterization

models

According to ISO standards, it is acceptable to select a category indicator anywhere along an

environmental chain between intervention and endpoint (Guinee et al. 2002). Hence the

aims set out in goal and scope play a large part in choosing the appropriate method. On the

basis that the intended audience of this study are assumed to be scientifically proficient, a

mid-point approach to characterisation of LCI emissions data is selected for the life cycle

impact assessment. The use of mid-point indicators over end-points is considered to be

more accurate as they are closer in the cause-and-effect chain to the source of emissions.

Many LCA practitioners are of the opinion that the availability of data is too limited to

extend an impact as far as an end-point such as the “amount of species killed per unit area

per year” (Bare et al, 2000).

Fundamentally, what the various impact assessment methods do is multiply the inventory

results by the appropriate characterisation factors yielding the “environmental profile”

which is then normalised (Guinee et al, 2002). The scope of this project only requires focus

on global warming potential, resource depletion and water usage however acidification will

also be included. Although a midpoint impact method is being deployed to characterise LCI

data, ISO 14044 standards require that the potential endpoint impacts must be explicitly

stated in the report. The endpoints associated with global warming potential are polar ice-

cap melting, sea-level rise and alteration prevailing weather patterns. Resource depletion

exhibits endpoints related to unsustainability with a decrease of these resources available to

future generations. An endpoint in this category might look like “amount of persons per

square metre unable to meet electricity requirements”. With an endpoint as speculative as

this it is easy to see why LCA practitioners believe that the current records are not robust

enough to extend an impact as far as endpoints (Bare et al, 2000). Water usage can result in

endpoints associated with drought, crop failure and species extinction for example “species

depleted per square meter per unit time” as the “EcoIndicator 99” characterisation method

in GaBi illustrates.

Table.9 details the key contributors within the life cycle of a wind turbine to each of these

respective impact categories.

19

Table 9: Sources of significant environmental impact within the wind turbine's life cycle

Unit Process Global Warming Resource Depletion Acidification Water Usage

Tower Manufacture Steel rolling/forging, material transport, electricity use

Steel extraction, diesel/oil use during transport.

Release of acidic gases during production/ combustion

Cooling during industrial processes, emissions to water.

Rotor Manufacture Cast iron/fibreglass processing, material transport, electricity use

Iron/Ferro-metals extraction, diesel/oil use during transport.

“ “ “ “

Nacelle Manufacture

Steel/Cast iron casting, material transport, electricity use

Steel/Iron extraction, diesel/oil use during transport

“ “ “ “

Inverter Manufacture

Copper/Aluminium processing, material transport, electricity use

Copper/Aluminium extraction, diesel/oil use during transport

“ “ “ “

Cable Manufacture PVC/Copper/Steel processing/casting, material transport, electricity use

Plastic derivative/Copper/Steel extraction, diesel/oil use during transport

“ “ “ “

Site Infrastructure Cement mixing/concrete casting, material transport, electricity use

Sand/Lime/Rock extraction, diesel/oil use during transport and earth works

“ “ Water used in concrete mix, emissions to water

Turbine Assembly Bolting/drilling/welding parts together.

Crane and JCB use of diesel

“ “ Emissions to water

Turbine Maintenance

Involves a combination of all the aforementioned manufacturing processes in scaled down form

Lubricating oil, diesel oil use during transport and site maintenance visits

“ “ Involves a combination of all the aforementioned manufacturing processes in scaled down form

Turbine Deconstruction

Electricity/diesel use in deconstruction, emissions from landfill

Diesel use in transport and crane/JCB processes

Release of acidic emissions during decomposition/ combustion

Emissions from landfill

20

Assignment of LCI results to selected impact categories

Table.9 shows that each of the unit processes contributes in some way to each of the four

impact categories chosen for this study. Characterisation factors will be assigned to each of

the emissions associated with these processes to express results as an environmental

impact quantity. This will be carried out using CML-2001.

Calculation of category indicator results:

Based on the fact that midpoint indicators are to be utilised to characterise GWP, resource

depletion and water usage in this model, the CML-2001 characterisation method was

deemed most appropriate for this study. This method created by scientists from Leiden

University in The Netherlands, is the most up-to-date method available in the educational

version of GaBi and was selected on the basis of its high ranking on Hauschild et al’s (2013)

assessment of the most reliable midpoint impact characterisation methods.

This model expresses GWP (100 year time horizon) in as a midpoint in terms kg of CO₂

equivalent. The CML characterisation factor is based on data from the Intergovernmental

Panel on Climate Change (BRE, 2005).

Resource depletion is related to the extraction of scarce minerals and fossil fuels and is

expressed as “abiotic resource depletion” in units of kg Sb equivalent. This unit takes into

account calculations of remaining reserves and the rate of extraction (BRE, 2005).

Acidification potential is expressed as kg SO₂-eq. Endpoints of this category are attributed to

acid rain and ecosystem impairment. Acidic gases such as NOx and SOx, released during the

various life cycle stages can react with moisture in the atmosphere, resulting in acid rain.

CML’s midpoint of “freshwater aquatic eco-toxicity”, measured in kg of dichlorobenzene

equivalent (kg of DCB-eq) to indicate how toxic releases from the life cycle of the wind

turbine can affect freshwater environments.

21

Resulting data after characterization:

Table.10 shows the values given by the CML-2001 characterisation method while figure.4

expresses these values as relative contributions.

Table 10: Life cycle impact assessment values

Global Warming (GWP100)

Abiotic Resource Depletion

Acidification Potential

Freshwater Aquatic Eco-Toxicity

Units kgCO₂-eq Kg Sb-eq Kg SO₂-eq kgDBC-eq

Tower Manufacture

63,600 3.03 298 1080

Rotor Manufacture 36,900 0.9 130 189

Nacelle Manufacture

30,000 1.22 126 479

Inverter Manufacture

75,300 12.8 322 707

Cable Manufacture 6,750 8.36 29.9 189

Site Infrastructure 59,100 0.0611 137 94.5

Turbine Maintenance

14,300 2.99 67 220

Turbine Deconstruction

1800 0.000182 6.7 9.18

Other 49350 0.0289 77.1 41.5

Total 337,000 29.4 1190 3010

Figure 4: Relative contribution of major turbine components per impact category

0% 20% 40% 60% 80% 100%

Global Warming(GWP100)

Abiotic ResourceDepletion

Acidification Potential

Freshwater Aquatic Eco-Toxicity

18.9

10.3

25.0

35.9

10.9

0.2

10.9

6.3

8.9

4.1

10.6

15.9

22.3

43.5

27.1

23.5

2.0

28.4

2.5

6.3

17.5

0.2

11.5

3.1

4.2

10.2

5.6

7.3

0.5

0.0

0.6

0.3

14.6

3.0

6.5

1.4 Tower

Rotor

Nacelle

Inverter

Cable

Site

Maintenance

End of life

Other

22

Normalisation

Normalisation is the expression of this profile relative to a given geographical region. In this

project it is acceptable to use a region as broad as Europe or even the world for this

purpose. This facilitates a clearer understanding of the magnitude of LCI results as they are

related to a specific population and time frame. CML-2001 carries this out automatically

hence all results are already normalised according to global (GWP) and European (Resource

depletion, acidification and water pollution) standards.

Grouping

Figure.4 has an “other” category highlighted in a green colour. This consists of an aggregate

of all electricity, thermal energy and transport processes utilised throughout the model.

Following a gravity/pareto analysis it was observed that these processes contributed a

negligible amount of emissions individually however when grouped together their impact

could be significant especially in terms of GWP (fig.4).

Data Quality Analysis

Gravity Analysis Figure.5 shows a pareto analysis of the wind turbine life cycle ranking the cumulatively

highest contributors to the left of the graph and the lowest contributors to the right.

Superimposed on top of this graph are the relative material quantities for each component

(orange line).

0

5

10

15

20

25

30

35

40

45

50

Pe

rce

nta

ge C

on

trib

uti

on

to

em

issi

on

s (%

)

Global Warming(GWP100)

Abiotic ResourceDepletion

AcidificationPotential

Freshwater AquaticEco-Toxicity

Figure 5: Pareto Analysis of the major turbine components

23

Despite having the highest amount of material input (71.1%), the “site” infrastructure ranks

only third on the list with a GWP contribution of 17.5%. The “tower” component ranks only

second in all categories except for “freshwater eco-toxicity” despite having a 13.3% share of

material inputs. The “inverter” production process ranks highest on the list despite having

only a 1.7% share of material inputs. This process along with the “cable” manufacturing

process ranks particularly highly in the abiotic resource depletion category.

Uncertainty Analysis Taking GWP as an example, Table.9 compares the emissions calculated from this study to

those in the literature. After a brief analysis it becomes evident that the overall emissions

have been grossly underestimated in this study.

Table 10: Comparative analysis with other wind turbine studies

This Study (2015)

Tremeac & Meunier (2009)

Ghennai (2012)

Crawford (2009)

Abeliotis et al (2014)

Unit kg CO₂-eq kg CO₂-eq kg CO₂-eq kg CO₂-eq kgCO₂-eq

Total emissions 337,000 820,467 1,400,000 1,844,000 872,000

Turbine Assembly 311,000 705,111 1,200,000 N/A 928,300

Turbine Maintenance

13400 N/A N/A N/A 405

Turbine Deconstruction

1710 -48.88888889 13095 N/A -70,200

Functional Unit 1 MW 1 MW 1MW 1MW 1MW

24

Life Cycle Interpretation

Significant Issues

Reiterating the goal of this project, the primary objective was to identify and analyse any

aspects of the wind turbine’s life cycle which have a considerable impact on the

environment. In order to address this, the analysis will follow the structure of the gravity

analysis carried out in the LCIA.

Secondly, the project set out to determine the benefits of offsetting grid use; and ultimately

intends to conclude whether the construction of a wind turbine is a worthwhile investment

in terms of saving on emissions. This will be explained using a scenario analysis outlining the

gradual increase in emissions as the Irish grid supplements the energy demand.

Inverter (and cable) manufacture: Aluminium and copper The gravity analysis carried out in figure.5 revealed that despite a minuscule share (1.7%) of

the overall amount of input materials, the production of the inverter component of the

turbine proved to be the costliest aspect of the life cycle in terms of emissions. At first

glance this outlier appeared to be the result of experimental error however another study

also noted that copper usage is particularly detrimental in terms of emissions. Figure.6

hones in further on the inverter process to reveal that a “copper from electrolysis” process

and an “Aluminium ingot” process are responsible for the poor environmental performance

of this component.

Figure 6: Relative contribution to emission for Inverter manufacture

Aluminium is responsible for the majority of acidification and GWP emissions. Despite being

the third most common element on Earth (hence its low contribution to resource

depletion), the extraction process for aluminium is extremely energy intensive

0

20

40

60

80

100

120

Acidification Abiotic resourcedepletion

GWP Freshwater eco-toxicity

Pe

rce

nta

ge C

on

trib

uti

on

of

em

issi

on

s (%

)

DE: Copper mix(99,999% fromelectrolysis) PE

EU-27:Aluminiumingot mix PE

25

(Environmental Literacy Council, 2015). According to the Environmental Literacy Council

(2015) copper is also extremely rare in its pure form (hence its high contribution to resource

depletion) and the electrolysis process used to purify is extremely energy intensive. This

observation can be reiterated by looking at the “cable” process in figure.5, which also has a

resource depletion outlier attributable to copper use. Wilburn (2011) notes the importance

of reducing the amount of copper in future wind turbines to improve their environmental

appeal. Despite this Wilburn did not report as large an anomaly as noted in this study.

Steel vs concrete This is where the experimental error comes in, the copper and aluminium processes used in

Gabi (appendix.1_inverter manufacture) are cradle-to-gate processes involving purification

of the two elements from electrolysis. Furthermore aluminium extraction requires large

amounts of strip mining and heavy industrial processes to separate it from the mineral

bauxite. The copper and aluminium used in industry is generally sourced from recycled scrap

to eliminate these costly processes from the supply chain (Environmental Literacy Council,

2015), something this model failed to do. With a pound-for-pound environmental

performance this low, it might go some way to explaining why some of the studies used to

compare against (Tremeac & Meunier,2009, Abeliotis et al, 2014), omitted the modelling of

the inverter component altogether. Green-washing?

Moving on to the next largest emitter, the “tower” process, accounting for only 13.3% of the

total amount of material yields the highest contribution overall to freshwater eco-toxicity.

This process also contributes higher in all impact categories over “site infrastructure” which

accounts for 71.1% of the total material inputs. Figure.7 identifies pre-cast concrete as the

main component of site infrastructure while steel is the predominant component of the

tower manufacture. Clearly steel has a more significant impact to the environment due to a

complex production process along with the use of rarer earth materials however it

maintains a much lower pound-for-pound environmental burden than aluminium or copper.

Figure 7: Concrete vs Steel emissions contribution

0

5

10

15

20

25

30

35

40

45

GWP Acidification ResourceDepletion

Freshwater Eco-toxicity

Re

lati

ve C

on

tib

uti

on

to

em

issi

on

s (%

)

EU-27: Pre-cast concretePE

DE: Steelbillet (100Cr6)PE

26

Expected trend and Nacelle anomaly For the most part the remaining processes in the life cycle appear to yield straight-forward

results. Figure.5 illustrates that low emissions coincide with low material inputs. The

processes such as turbine maintenance, nacelle and rotor manufacturing have relatively less

intensive production processes also, keeping emission low. End-of-life is not shown in this

figure as the associated emissions were below the cut-off point in this instance. There is a

slight blip in the maintenance stage which is also attributable to copper and aluminium

replacement parts.

The only anomaly worth investigating further is the nacelle’s relatively high contribution to

freshwater eco-toxicity. Steel appears to be the main culprit according to figure.8 which

seems to follow the trend set by this material in figure.7.

Figure 8: Nacelle Freshwater contribution

Wind energy compared to grid use Up until this point, there has been little mention of emissions associated with the Irish grid.

This is due to the default scenario of the turbine operating at optimum conditions hence

there was no input from the Irish grid. Besides saving money and increasing energy security

the main reason for an Irish enterprise to invest in a wind turbine is to reduce their

environmental impact during the generation of electricity. In order to provide an answer to

this question figure.9 shows a comparison between wind and grid energy emissions.

Flow s

Diagram:Nacelle_Manufacture - Inputs/Outputs

DE

: S

teel b

illet (1

00C

r6)

PE

DE

: C

ast iron p

art

(auto

motiv

e)

PE

<p-a

gg>

FR

: E

lectr

icity

grid m

ix (

pro

ductio

n m

ix)

CM

L2001 -

Apr.

2013, F

reshw

ate

r A

quatic

Ecoto

xic

ity P

ot. (

FA

ET

P in

f.)

[kg D

CB

-Equiv

.]

450

400

350

300

250

200

150

100

50

0

27

These graphs change according to wind speed, rotor efficiency and wind-hours for each

respective scenario. The data for each scenario is available in table.7. Figure.9 displays a

slight reduction in emissions as wind speed increases from 3-6 m/s representing less of a

reliance on the grid. From 6-13 m/s the turbine is operating at optimum conditions and

100% of the energy demand is being supplied by wind power. The emissions associated with

these speeds are the total emissions from the turbine life cycle. At 14-m/s onwards, the

rotor efficiency reduces and the wind frequency lowers meaning the grid kicks in to

0

100

200

300

400

500

600

700

3m/s

4m/s

5m/s

6m/s

7m/s

8m/s

9m/s

10m/s

11m/s

12ms

13m/s

14m/s

15m/s

16m/s

Re

lati

ve c

on

trib

uti

on

to

em

ssio

ns

(%)

Wind Speed scenario

GWP

Acidification

Freshwater Eco-toxicity

Resource Depletion

0

2000

4000

6000

8000

10000

12000

14000

16000

14 m/s 15 m/s 16 m/s 17 m/s 18 m/s 19 m/s 20 m/s 21 m/s

Re

lati

ve c

on

trib

uti

on

to

em

issi

on

s (%

)

Wind Speed Scenario

GWP

Acidification

FreshwaterEco-Toxicity

Resourcedepletion

Figure 9: Wind speed scenarios impact on emissions

28

supplement energy demand. In each scenario thereafter, acidification, resource depletion

and GWP emissions increase dramatically shooting up to between 10,000 and 14,000%.

Figure.10 shows absolute values for the influence of wind speed on the four impact

categories. At 13 m/s all emissions are purely from wind energy but from 15 m/s onwards,

the emission values increase over 1000 times to that of wind energy.

Table 11: Influence Irish grid has on emissions.

Units 13 m/s 15 m/s 17 m/s 19 m/s 21 m/s 23 m/s

GWP kg CO2-Equiv.

337296.6 1002049 3517279 13973946 65032255 359535158.8

Acidification pot.

kg SO2-Equiv.

1192.054 3125.94 10443.2 40863.55 189401.5 1046164.071

Freshwater Eco-Tox

kg DCB-Equiv.

3007.201 3358.496 4687.695 10213.63 37195.92 192829.0055

Resource Depletion

kg Sb-eq 2957556 11119319 42001060 1.7E+08 7.97E+08 4413153312

Evaluation

The results show favourable emissions data for the wind energy industry however there is a

major issue with the results of this study which must be taken into account before

conclusions can be made.

I. Completeness As noted in table.10, this study has grossly underestimated life cycle emissions for a wind

turbine and a functional unit 1MW of electricity. The results from this study are between 3-8

times lower than those calculated by recent studies on wind turbines using GWP as a

reference.

This error is most likely due to the over-simplification of the model. The nacelle for example

consists of 1000 different parts in real life. This study attempts to quantify these parts by

categorising them into their core material. This leads to the omission of a vast amount of

processes, which adds to the overall emissions. In addition, the use of stamping and bending

processes in replacement of forging and casting processes probably underestimates

emissions also.

Another inconsistency associated with this model is the improper modelling of recycling.

Had the recycling phase been executed properly, the emissions for the likes of copper and

aluminium would have been lowered significantly improving the integrity of the model.

Referring back to table.10 once more, some of the studies have minus figures for end-of-life

phase which represents credit for recycling.

29

II. Sensitivity The sensitivity analysis used to demonstrate the impact of grid emissions proves that the model responds as it is supposed to with respect to changes in its parameters. Wind speed, rotor efficiency and wind hours all alter according to the site specific Weibull distribution outlined in table.7. Figure.9 and table.10 demonstrate how the emissions steadily rise as the three parameters used in the sensitivity analysis alter.

III. Consistency Every effort was taken to ensure spatially and temporally accurate data was utilised

whenever possible. Many of the pre-existing processes in GaBi allow the user to select

geographically relevant processes. The Irish and French grid mixes for example are derived

from data as recent as November 2014. Some of the production processes however were

from the NREL database and hence were based on American data.

With regards to impact categories, the CML-2001 method should have accurately

normalised data to the relevant spatial standard for each impact category.

Conclusions

Despite a significant degree of experimental error, hotspots could be identified in

the wind turbine’s life cycle. Aluminium and copper proved to be among the most

notorious material for emissions in GWP, resource depletion, water eco-toxicity and

acidification due to their energy-intensive extraction processes and electrolysis

processes used for purification.

Steel was the second highest emitter and this was more proportionate to the

amount of this material required.

According to these results wind energy can definitely be considered a green

technology when the entire life-cycle is taken into account.

This observation is marred by the fact that the model grossly underestimated overall

emissions.

Substituting the overall GWP emissions from this study with those from Crawford

(2009), the Irish grid yields emissions 195 times greater than total emissions from

wind energy. Based on these figures, wind energy unequivocally deserves the title of

a “green technology”.

Limitations and Recommendations

The key limitation with this study is that the model is not of a sufficient resolution to

represent the life cycle of a wind turbine accurately. A key recommendation is that more

accurate activity data be sought for the wind farm in Lusk Co. Dublin as well as on industry

data on the wind turbine process itself.

Secondly the layout of the model in GaBi requires a rethink in order to incorporate recycling

phases appropriately.

30

A second or third sensitivity analysis should have been carried out to analyse other portions

of the model however due to a modelling over-sight this proved too difficult to perform. The

oversight consisted of leaving transport and recycling processes out of the naming hierarchy

applied to manufacturing phases. This meant that when a sensitivity analysis was attempted

the respective transport processes were indecipherable meaning it was impossible to know

which leg of the transport was being edited. To rectify this would have meant to go back

and redevelop the model/ There was insufficient to perform this hence only one sensitivity

analysis will suffice.

31

Critical Review The purpose of this study was to apply the LCA technique to a wind turbine in Co. Dublin to

determine whether wind energy is worthy of its title of a “green technology”. The aims were

to highlight any significant processes in the life cycle with considerable environmental

effects as well as demonstrating the amount of fossil fuel emissions offset by this

technology. The project initially set out to cover all process cradle-to-grave however due to

a modelling error, the system boundary had to be refined to exclude the recycling phase.

This omission along with the rationale behind it was illustrated very clearly and explicitly.

The system diagram makes good use of colour to illustrate clearly the various unit processes

and how they link up.

The life cycle inventory phase seems to be executed reasonably well with an abundance of

tables outlining the inputs to the model. The activity is predominantly derived from

secondary sources from industry analyses. All sources are clearly referenced.

The model itself appears to be the biggest issue with the project, the user had difficulty

executing the end-of-life phase hence credit for recycling cannot be included in the model.

In addition, the model is too simplified hence not all emissions are accounted for. These

errors are clearly documented.

The LCIA phase adequately described the endpoint categories and stated which midpoints

were to be used in the project. The CML-2001 characterisation method was selected on

recommendation from a journal article. Results were compiled neatly on an innovative

graph which clearly shows material inputs against impact categories.

The interpretation highlighted aluminium and copper as the key impacts to the turbine life

cycle. Some of these anomalous figures were attributed to modelling error however some

studies in the literature back this observation up to suggest these material have a relatively

large environmental impact. These sources were reference appropriately.

Despite an abundance of experimental errors, the study can confidently conclude wind

energy is a green technology; with emissions approximately 195 times lower than the Irish

grid mix. Hence the objectives of the study were accomplished.

Appropriate recommendations for improvements were made and screenshots of the model

were included in the appendices, adding to the transparency of the project, leaving it open

to a more honest interpretation.

32

Appendices

Appendix 1 - Raw material extraction and manufacturing

33

34

Appendix 2 – Turbine assembly and deconstruction

35

Appendix 3 – Turbine maintenance and use phase

At optimum wind speed of 10

m/s, site processes 100% powered

by wind energy

At a less efficient wind speed of

15 m/s, site processes only ~40%

powered by wind energy

36

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Hauschild, M. Z. Goedkoop, M. Guinee, J. Heijungs, R. Huijbregts, M. Jolliet, O. Margni, M. De Schryver, A. Hmbert, S. Laurent, A. Sala, S. Pant, R. (2013) ‘Indentifying best existing practice for characteriization medeling in life cycle impact assessment’, Life Cycle Assess, 18, 683-697.

Iso14044. (2006) ‘Environmental management- Life cycle Assessment- Requirements and guidelines’, British Standard, UK.

Martinez, E. Sanz, F. Pellegrini, S. Jimenez, E. Blanco, J. (2009) ‘Life cycle Assessment of a multi-megawatt wind turbine, Renewable Energy, 34, 667-673.

Pennington, D., Potting, J., Finnveden, G., Lindeijer, E., Jolliet, O., Rydberg, T. and Rebitzer, G. (2004) 'Life cycle assessment Part 2: Current impact assessment practice', Environment international, 30(5), 721-739.

Rebitzer, G., Ekvall, T., Frischknecht, R., Hunkeler, D., Norris, G., Rydberg, T., Schmidt, W.-P., Suh, S., Weidema, B. P. and Pennington, D. (2004) 'Life cycle assessment: Part 1: Framework, goal and scope definition, inventory analysis, and applications', Environment international, 30(5), 701-720.

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37

Wilburn, D.R (2011) ‘Wind energy in the United States and materials required for the land-based turbine industry: From 2010 through 2030’, United States Geological Survey, Scientific Investigations report, 5036.