the climate change mitigation potential of the solar pv industry: a life cycle perspective
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MSc Thesis, Imperial College London, 2009TRANSCRIPT
IMPERIAL COLLEGE LONDON
Faculty of Natural Sciences
Centre for Environmental Policy
The climate change mitigation potential of the solar PV industry: a life
cycle perspective
By
Gregory Briner
A report submitted in partial fulfilment of the requirements for
the MSc and/or the DIC.
September 2009
DECLARATION OF OWN WORK
I declare that this thesis:
The climate change mitigation potential of the solar PV industry: a life cycle
perspective.
is entirely my own work and that where any material could be construed as the work of
others, it is fully cited and referenced, and/or with appropriate acknowledgement given.
Signature:.....................................................................................................
Name of student GREGORY BRINER
Name of supervisors DR N. J. EKINS-DAUKES and DR T. COCKERILL
AUTHORISATION TO HOLD ELECTRONIC COPY OF MSc THESIS
Thesis title: The climate change mitigation potential of the solar PV industry:
a life cycle perspective
Author: Gregory Briner
I hereby assign to Imperial College London, Centre of Environmental Policy the right to
hold an electronic copy of the thesis identified above and any supplemental tables,
illustrations, appendices or other information submitted therewith (the “thesis”) in all
forms and media, effective when and if the thesis is accepted by the College. This
authorisation includes the right to adapt the presentation of the thesis abstract for use in
conjunction with computer systems and programs, including reproduction or publication
in machine-readable form and incorporation in electronic retrieval systems. Access to
the thesis will be limited to ET MSc teaching staff and students and this can be
extended to other College staff and students by permission of the ET MSc Course
Directors/Examiners Board.
Signed: __________________________
Name printed: Gregory Briner
Date: 9th
September 2009
Abstract
There is currently great interest in the potential of using solar photovoltaic (PV)
modules to mitigate greenhouse gas (GHG) emissions from the electricity-generation
sector. While GHG emissions from solar PV are negligible during operation, emissions
are still produced from the manufacture of solar PV systems when fossil fuels are used
to power the supply chain. For this reason it is necessary to consider all stages of the
life cycle when assessing the potential of solar PV to mitigate climate change.
An overview of solar PV technology types, production processes and PV industry trends
is presented. Previous life cycle assessments (LCA) of the levelised GHG emissions (g
CO2 kWh-1
) and CO2 mitigation potential (tonnes CO2 kWp-1
) of crystalline silicon and
cadmium telluride PV systems are reviewed and the reasons for discrepancies between
them are analysed. A model is developed to determine the sensitivity of levelised GHG
emissions and CO2 mitigation potential to technology type, production supply mix,
displaced supply mix and irradiance. The levelised GHG emissions are found to be in
the range 2-200 g CO2 kWh-1
, depending on the assumptions used. The levelised CO2
emissions from transportation are also examined and estimated to lie in the range 0-12 g
CO2 kWh-1
. The CO2 mitigation potential of crystalline silicon PV systems ranges from
–3 tonnes kWp-1
in Norway to 45 tonnes CO2 kWp-1
in Australia. It is found to be
positive in all cases where the PV module output is used to displace fossil fuels.
This report presents a new metric termed the „annual net CO2 balance‟ (Mt CO2 yr-1
),
which takes into account the impact of PV industry growth. It is estimated that the
annual net CO2 balance of the PV industry was -0.8 Mt CO2 in 2007 and -5 Mt CO2 in
2008. Future projections of this figure for the next 10-20 years are shown, based on
different potential scenarios. The net CO2 balance of the PV industry could be
improved by (1) curtailment of industry growth, (2) increased production process
efficiency, (3) increased use of low-carbon sources of energy for PV production, and (4)
stimulation of PV markets in sunny countries with high carbon intensities. The author
advocates pursuit of the latter three options.
Acknowledgements
I would like to thank Ned Ekins-Daukes for both his excellent supervision during my
project and for involving me in wider Quantum Photovoltaics Group activities. I would
like to thank Tim Cockerill for his helpful advice and guidance. I am also grateful to
Konstantinos Theodoropoulos for his input to my project.
Table of Contents
Abbreviations ........................................................................................................................................... i
Parameter Symbols .................................................................................................................................. ii
Unit Conversion Guide ............................................................................................................................ ii
1 Introduction ............................................................................................................. 1
1.1 Why this study is needed ........................................................................................................... 1
1.2 Aim and objectives .................................................................................................................... 2
1.3 Potential applications ................................................................................................................ 3
2 Background .............................................................................................................. 5
2.1 Solar PV technology ................................................................................................................. 5
2.2 Selected trends in the solar PV industry .................................................................................... 9
2.3 A life cycle perspective ........................................................................................................... 15
3 Methodology .......................................................................................................... 23
3.1 System boundary ..................................................................................................................... 23
3.2 Model equations ...................................................................................................................... 25
3.3 Limitations of the model ......................................................................................................... 28
4 Results and Analysis ............................................................................................. 30
4.1 Levelised CO2 emissions ......................................................................................................... 30
4.2 CO2 mitigation potential ......................................................................................................... 39
4.3 Annual net CO2 balance .......................................................................................................... 49
5 Discussion ............................................................................................................... 57
5.1 The importance of a life cycle approach ................................................................................. 57
5.2 Technological solutions .......................................................................................................... 57
5.3 Solar PV must not displace renewables or nuclear ................................................................. 60
5.4 Bringing together economic and environmental objectives .................................................... 61
6 Conclusions ............................................................................................................ 64
References ...................................................................................................................... 66
Appendix 1 - Carbon intensity and irradiance by country ....................................... 70
Appendix 2 - Model parameters .................................................................................. 71
Appendix 3 - Derivations of model equations ............................................................. 73
i
Abbreviations
a-Si Amorphous Silicon
AM Air Mass
BoS Balance of Systems (the inverter, cabling and module support structure)
CARMA Carbon Monitoring for Action
CdTe Cadmium Telluride
CIS Copper Indium Selenide
CIGS Copper Indium Gallium Selenide
CVD Chemical Vapour Decomposition
EC European Commission
EPBT Energy Payback Time
EPIA European Photovoltaic Industry Association
EVA Ethylene Vinyl Acetate
FBR Fluidised Bed Reactor
GHG Greenhouse Gas
GIC Global Installed Capacity
HVDC High Voltage Direct Current
IEA International Energy Agency
LCA Life Cycle Assessment
MG-Si Metallurgical-grade Silicon
mono-Si Monocrystalline Silicon
multi-Si Multicrystalline Silicon
poly-Si Polycrystalline Silicon
PR Performance Ratio
PV Photovoltaic
STC Standard Test Conditions (1,000 W m-2
, 25°C, AM 1.5)
UCTE Union for the Co-ordination of Electricity Transmission
Wp Peak Watt (the power output under standard test conditions)
ii
Parameter Symbols
Symbol Unit Parameter
Ce1 g CO2 kWh-1
Carbon intensity of supply mix used in production
Ce2 g CO2 kWh-1
Carbon intensity of displaced supply mix
Cth g CO2 kWh-1
Carbon intensity of heat generation
Cship g CO2 kg-1
km-1
Carbon intensity of transportation by ship
Ctruck g CO2 kg-1
km-1
Carbon intensity of transportation by truck
dship km Distance travelled by ship
dtruck km Distance travelled by truck
Ee kWhfinal m-2
Quantity of electricity used in production
Eth kWhprimary m-2
Quantity of heat used in production
GIC kWp Global installed capacity
I kWh m-2
yr-1
Irradiance
L yrs Module lifetime
m kg m-2
Mass per square meter of module
PR - Performance Ratio
r - Rate of growth of global installed capacity
ηm - Module efficiency
ηtd - Efficiency of transmission and distribution network
Unit Conversion Guide
1 kWh = 3.6 MJ
1 MJ = 0.278 kWh
1 kWh m-2
yr-1
= 0.114 W m-2
1 W m-2
= 8.760 kWh m-2
yr-1
1 tonne = 106 g
1 Mt = 106 tonnes
1 g CO2 = (12/44) g C
1 g C = (44/12) g CO2
1 m2 = (ηm) kWp
1 kWp = (1/ ηm) m2
1 g CO2 kWp-1
= (ηm) g CO2 m-2
1 g CO2 m-2
= (1/ ηm) g CO2 kWp-1
1
1 Introduction
1.1 Why this study is needed
1.1.1 The link between climate change mitigation and life cycle assessment
Solar photovoltaic (PV) modules have been used to generate electricity from sunlight
for many decades. They offer many advantages over conventional forms of electricity
generation: they are clean, offer energy security (in the sense that the „fuel‟ is
effectively inexhaustible and is not imported from other countries), require little
maintenance and can be used in remote locations away from existing power grids.
More recently the climate change issue has renewed interest in solar PV modules as a
way to cut greenhouse gas (GHG) emissions from the electricity generation sector.
Several countries, including Germany, Japan and the USA, now have major financial
support schemes in place for solar PV projects as part of their national strategies to
reduce GHG emissions.
In order to assess the potential of solar PV for mitigating climate change it is necessary
to consider the technology from a life cycle assessment (LCA) perspective. The reason
for this is that while GHG emissions from solar PV modules are negligible during
operation, GHGs are still emitted from the production (and, in some cases, the
decommissioning) stages of the life cycle. Solar PV is by no means alone in this respect
– GHGs are emitted during the life cycle of all electricity-generating technologies.
1.1.2 Assessing the sensitivity of LCA results using numbers, not words
While there are many studies of the levelised life cycle GHG emissions of solar PV in
the academic literature, few explore the impact that changing the input parameters
would have on the LCA results. Some provide vague qualitative statements about this
impact, such as the following two examples from de Wild-Scholten and Alsema (2005)
and Fthenakis et al. (2008):
2
‘...analysts should be aware of the large influence that the electricity supply mix
for the solar grade silicon process will have on final impact results.’
‘Other electricity generation and production related-parameters ... are also
advancing in parallel and would also result in reduced emissions.’
This project attempts to fill this perceived gap in the literature by providing quantitative
data to replace these qualitative statements. How large is the influence of the electricity
supply mix on levelised GHG emissions? Which other „production-related parameters‟
are advancing in parallel, and what are the limits to the technical improvements that can
be made? And finally, what is the influence of these parameters on the CO2 mitigation
potential (tonnes CO2 kWp-1
), both for individual modules and for the industry as a
whole? To answer these questions, a sensitivity study into the levelised GHG emissions
and CO2 mitigation potential of solar PV modules was undertaken to identify the most
important factors. Emissions from transportation – an issue that is largely ignored in
previous LCA studies on solar PV – were also examined.
1.1.3 New work on the implications of industry growth
A new LCA metric termed the „annual net CO2 balance‟ was developed for this project
in response to the absence of any metrics in the LCA literature that take into account
industry growth. This work on industry growth was ground-breaking because, as far the
author is aware, it is the first time anyone has highlighted the dramatic implications of
the link between the rate of industry growth and the net CO2 balance of the PV industry.
1.2 Aim and objectives
The overall aim of the project was to answer these two questions:
1. What is the potential of the solar PV industry to mitigate greenhouse gas
emissions from the electricity generation sector?
3
2. What are the limits or barriers currently preventing the industry from
achieving this potential?
In order to achieve this overall aim the research objectives were:
To assess the present status of solar PV technologies and determine the quantity
of greenhouse gases emitted during the life cycle of these technologies
To assess the life cycle CO2 mitigation potential of solar PV modules that are
manufactured and installed in different countries
To design a new life cycle metric for assessing the net CO2 balance of the PV
industry that takes into account industry growth
To examine the sensitivity of the net CO2 balance to industry growth rate,
carbon intensity of production supply mix and distribution of installed capacity
To provide recommendations for effective ways to improve the net CO2 balance
of the PV industry in the future
1.3 Potential applications
The Clean Development Mechanism (CDM) and Joint Implementation (JI) programme
allow participating nations in the Kyoto Protocol to earn carbon credits by investing in
clean energy projects in other countries. The results of this project could be valuable for
calculating the climate change mitigation potential of solar PV projects funded via these
mechanisms. This project could also be used to revise estimates of the costs of CO2
abatement from solar PV in different countries, which would aid the development of
national strategies to mitigate climate change.
In most countries electricity from solar PV is currently more expensive than
conventional sources. A high level of financial investment is needed to stimulate the
solar PV market and expand the PV industry in order to lower the cost of solar PV in
the longer term. Unfortunately, several of the countries that can currently afford this
initial financial investment, such as Germany and Japan, are not the best places to install
solar PV from a climate change mitigation point of view. This project emphasises the
4
need for policies that not only stimulate investment in solar PV technology, but also
ensure that this technology is deployed in the most beneficial locations first in order to
maximise its climate change mitigation potential.
5
2 Background
This background chapter is split into three sections:
Solar PV technology
This section provides an overview of the different technologies available and
how they are manufactured.
Selected trends in the solar PV industry
This section presents a few key industry trends that are relevant to a discussion
of climate change mitigation by solar PV.
A life cycle perspective
This section describes the four stages of LCA and the different life cycle metrics
that are available to assess the environmental performance of energy
technologies. It contains a literature review of previous LCA studies on solar
PV.
2.1 Solar PV technology
2.1.1 Different types of solar PV technology
There are several different types of solar PV technology. They fit into three main
categories: crystalline silicon, thin film technologies and future technologies. Figure 1
shows the different technology types in each category.
6
Figure 1 Different solar PV technology types
This project focuses on monocrystalline silicon (mono-Si), multicrystalline silicon
(multi-Si), silicon ribbon (ribbon-Si) and cadmium telluride (CdTe) PV modules.
Between them these technologies accounted for over 90% of the PV module market in
2007 (IEA, 2008).
7
2.1.2 Production processes
Figure 2 Production steps for crystalline silicon PV modules
Figure 2 shows how crystalline silicon PV modules are produced. All crystalline silicon
PV cells begin life as silica, which is reduced in an electric arc furnace to form
metallurgical-grade silicon (MG-Si). The MG-Si is then purified to make
polycrystalline silicon (poly-Si). There are several different methods by which MG-Si
can be purified and the quality of the resulting poly-Si varies according to the method
used, with the most expensive and energy intensive methods producing the highest
grade poly-Si. A summary of the different methods available is shown in Table 1.
polycrystalline silicon
8
Table 1 Poly-Si production processes (Braga et al., 2008; Jungbluth et al., 2008;
Mehta and Bradford, 2009)
Name of Process Description Energy Demand
/ kWh kg-1
Purity of
Poly-Si
Market
share
(2007)
Siemens
Decomposition of trichlorosilane (SiHCl3) by
chemical vapour decomposition (CVD) on an
inverse U-shaped hot filament at 1,000°C, batch
process
200 High 4%
Modified Siemens
Decomposition of silane (SiH4) by CVD on an
inverse U-shaped hot filament at 800°C, batch
process
140 Medium 61%
Fluidised Bed Reactor
(FBR)
A gaseous mixture of silane (SiH4) and hydrogen
flows over a bed of silicon seed grains at 500°C and
causes deposition of silicon on the surface of the
grains, continuous process
20 Medium 25%
Upgraded MG-Si Direct purification of MG-Si which avoids use of
silane or trichlorosilane – various physical and
chemical routes under development
~30 Low 5%
For mono-Si and multi-Si technologies the poly-Si is crystallised or cast into ingots,
which are then sawn into wafers. The wafers are etched and metallisation paste is
applied in the grooves to make cells. Mono-Si wafers are made from large single
crystals and produce high efficiency PV cells (15-18%), but the process used to grow
them (termed the Czochralski process) is energy-intensive and expensive. Multi-Si
ingots consist of a large number of smaller crystals and require less energy to create, but
the resulting PV cells have a lower efficiency (13-15%). For ribbon-Si the purified
poly-Si is cut directly into silicon ribbons, which are then used to make cells. This
process is the least energy-intensive of all, but the efficiency of the resulting PV cells is
also the lowest of the crystalline silicon technologies (12-13%). CdTe cells are made by
rapid deposition of a thin film of gaseous cadmium telluride onto a glass substrate,
producing cells with an efficiency of around 10%.
The cells are then assembled together and encapsulated in glass and ethylene vinyl
acetate (EVA) to produce modules. The modules are housed in an aluminium and steel
frame. An inverter, cabling and module support structure for roof-mounting complete
the PV system.
9
2.2 Selected trends in the solar PV industry
2.2.1 Rapid industry growth and decreasing costs
Figure 3 shows how the global solar PV industry has been booming in recent years,
with an average annual growth rate of over 40% between 1998 and 2008 (Mehta and
Bradford, 2009). Since the last quarter of 2008, however, the growth of most global
manufacturing industries has been slowed down by the global economic downturn and
the solar PV industry has been no exception.
Figure 3 Global solar PV installed capacity 1998-2008 (EPIA, 2009)
This rapid industry growth is driving down manufacturing costs. The average cost of a
domestic-scale PV system in Germany in 2008 (including both module and installation
costs) was 4.4 €/Wp (3.8 £/Wp) (Konstantinos, pers. comm., 2009). This is projected to
halve to around 2 €/Wp (1.7 £/Wp) by 2020.
While still high, the cost of electricity from solar PV modules is falling more rapidly
10
than for any other electricity-generating technology. Nemet (2006) identifies the most
important factors driving reductions in module costs since 1980 as increasing plant sizes,
increasing module efficiencies and decreasing silicon prices.
Boyle (2004) shows that the average cost of electricity from a domestic grid-connected
crystalline silicon system in Britain was 34-76p kWh-1
in 2004, but suggests that this
could fall to 10-16p kWh-1
by 2020. This is similar to the current price of grid
electricity for residential final-users, so if this prediction holds true then grid parity will
be achieved for small-scale PV systems offsetting residential electricity demand in the
next 10 years.
2.2.2 Distribution of installed capacity
The main factor that has determined the present distribution of global installed capacity
has been the level of political support and financial incentives offered by different
national governments. Figure 4 shows how Germany currently has the largest
proportion of global installed capacity on a cumulative basis, despite not being a
particularly sunny country, because the German government introduced a generous
feed-in tariff for solar PV as part of its „100,000 roofs‟ programme. Strong political
support and financial incentives in Spain, the USA and South Korea explain the
significant amount of new capacity installed in these countries in 2008, as shown in
Figure 5. In Britain, a country with similar irradiance levels to Germany, the level of
financial support for solar PV has generally been lower and the cumulative installed
capacity reached just 23 MWp in 2008 (IEA, 2009).
11
Figure 4 Cumulative installed capacity by country in 2007
(EPIA, 2009). Values shown are in MWp. Total = 9,164 MWp.
Figure 5 Annual installed capacity by country in 2008 (EPIA,
2009). Values shown are in MWp. Total = 5,560 MWp.
12
The numbers presented in Figure 4 and Figure 5 are for grid-connected panels. In the
early days of the PV industry, solar PV was mainly used for off-grid applications such
as calculators or to power systems in remote locations such as satellites. However, solar
PV has since entered the mainstream electricity market and over 90% of cumulative
installed capacity is now grid-connected (IEA, 2008).
2.2.3 Market share of thin film technologies
Figure 6 shows how many companies plan to ramp up production of CdTe and other
thin film technologies over the next few years, because the relatively low material and
energy inputs give them a manufacturing cost advantage over traditional silicon
technologies. This is relevant to a discussion of climate change mitigation because it
means that the average quantity of electricity used in production will decrease as the
proportion of thin film technologies in the cumulative installed capacity increases.
Although the market share of crystalline silicon is projected to decrease, the volume of
production of crystalline silicon modules will continue to increase in absolute terms.
Figure 6 Global installed capacity by technology type in 2007, and projections for
2012 (EU JRC, 2008; Mehta and Bradford, 2009). The category „Other‟ includes a-Si
and CIS thin film technologies.
(projected)
13
2.2.4 Location of production
Table 2 shows how Germany, Japan, the USA and China are currently the leading
producers in the PV supply chain.
Table 2 Leading PV producers by country in 2007 (IEA, 2008; First Solar, 2009)
Stage in supply chain Leading producers
Poly-Si USA
Japan
Germany
Ingots and wafers Norway
Germany
UK
Japan
Cells and modules China
Japan
Germany
Taiwan
CdTe modules USA
Germany
Malaysia
BoS components Germany
Austria
Japan
USA
One of the most important stages of the supply chain from a climate change mitigation
point of view is the energy-intensive poly-Si production stage. Figure 7 shows the
leading poly-Si producers in 2007 and projections for 2012, which show how a large
number of new poly-Si production facilities in China, Russia and South Korea are
expected to come online between 2007 and 2012. Mehta and Bradford (2009) also
predict that by 2012, 50% of crystalline silicon cells will be produced in China and
14
Taiwan. This has important implications for the climate change mitigation potential of
the solar PV industry because the electricity generation sector in China is currently coal-
based and has a high carbon intensity.
Figure 7 Leading poly-Si producers in 2007, and projections for 2012 (Bradford, 2008)
(projected)
15
2.3 A life cycle perspective
2.3.1 Basic principles of life cycle assessment
The aim of a life cycle assessment is to assess the environmental impacts that occur at
all stages of the life cycle of a product, covering the complete chain of events from the
„cradle‟ to the „grave‟.
Figure 8 The four stages of life cycle assessment (ISO 14040, 2006)
Figure 8 shows the four stages of a life cycle assessment, as laid out by the International
Organisation for Standardisation (ISO). A description of what must be done at each
stage is shown in Table 3.
16
Table 3 Descriptions of the four stages of life cycle assessment (ISO 14040, 2006)
Stage Description
Goal and scope definition Define the goal and system boundary of the study.
Inventory analysis Construct a model of the product life cycle with all the
environmental inflows and outflows at each stage.
Impact assessment Calculate the emissions and resource consumption of each
component in the life cycle inventory.
Interpretation Draw conclusions about the environmental impact of the
emissions and resource consumption of the product.
2.3.2 Metrics for evaluating environmental performance
There are several different life cycle metrics that may be used to assess the
environmental impacts of electricity-generating technologies. These include:
Levelised GHG emissions
CO2 mitigation potential
Greenhouse gas return on investment
Energy payback time
Energy return on investment
SOx, NOx and PM10 emissions
Heavy metal emissions
The metrics examined in this project were the levelised GHG emissions, the CO2
mitigation potential and a new metric, the annual net CO2 balance, as these were
deemed to be the most useful ones for a discussion of climate change mitigation.
It is important to remember that these are only three of the many metrics that may be
used to compare the overall environmental performance of electricity-generating
technologies. It depends on how the different categories of environmental impact are
17
weighted as to what overall policy conclusions can be drawn from any LCA study.
2.3.3 Levelised GHG emissions
The levelised GHG emissions (g CO2-eq kWh-1
) are the quantity of greenhouse gases
emitted at all stages of the life cycle of an electricity-generating technology, divided by
its lifetime output of electricity. In the case of the life cycle of a solar PV module,
where operational emissions are negligible and the decommissioning step is excluded,
the levelised GHG emissions are simply:
output Lifetime
emissionsGHG Capital emissionsGHG Levelised
The term „capital GHG emissions‟ is used in this report to refer to the total greenhouse
gas emissions, in g CO2-eq m-2
, which arise in connection with the production of a solar
PV module.
There is a wide range of estimates of the levelised GHG emissions for solar PV in the
literature. A review of estimates from different countries using a range of government,
industry and academic sources was conducted for this project. The results are presented
in Figure 9. For comparison, the levelised GHG emissions of other electricity-
generating technologies are shown in Figure 10.
18
Figure 9 Estimates of levelised GHG emissions of solar PV in the literature
0
50
100
150
200
250
300Le
velis
ed
GH
G e
mis
sio
ns
/ g
CO
2-e
q k
Wh
-1
CdTe
19
Figure 10 Levelised GHG emissions of solar PV and other electricity-generating technologies (Alsema et al., 2006; Raugei et al., 2007)
20
An examination of the assumptions made in each study revealed that the main reasons
for discrepancies between the LCA results for solar PV are:
the system boundaries are different (e.g. the module frame and BoS components
are included in some but excluded in others)
different irradiance values have been used
different assumptions have been made about the grid supply mix
old production data from the 1980s or 1990s has been used
Pehnt (2006) provides a way of splitting these impacts into two categories by
identifying two different types of LCA input parameter: background system parameters
and technology-specific parameters. Background system parameters are those that are
common to other LCA studies, such as the carbon intensity of electricity generation. A
decrease in national carbon intensity will reduce the levelised CO2 emissions of both
wind and solar PV, for example, so the overall ranking order of these technologies
remains the same. An example of a technology-specific parameter is solar PV module
efficiency. An increase in module efficiency reduces the levelised CO2 emissions of
solar PV but not wind.
Pehnt defines imported impacts on LCA results as those due to changes in background
system parameters, and inherent impacts as those due to changes in technology-specific
parameters. Examples of imported and inherent impacts on the LCA results for solar
PV are shown in Table 4. Building on the work of Pehnt, this project produced
quantitative data on both the imported and inherent impacts on LCA results for solar PV.
21
Table 4 The difference between imported and inherent impacts
Imported Inherent
Definition Impact due to a change in a background
system component (affects LCA results for
other technologies)
Impact due to a change in a technology-
specific parameter (affects LCA results
for solar PV only)
Examples Production supply mix
Displaced supply mix
Transmission and distribution network
efficiency
Irradiance
PV module efficiency
PV module lifetime
PV module production energy demand
2.3.4 CO2 mitigation potential
The levelised GHG emissions alone do not say anything about how much CO2 is
„saved‟ (in terms of prevented emissions) by solar PV, because they are the same
whether solar PV displaces coal, gas or any other technology. The CO2 mitigation
potential metric, on the other hand, takes the carbon intensity of the displaced generator
into account and rewards the installation of solar PV in countries that have both a high
carbon intensity of electricity generation and high irradiance.
The CO2 mitigation potential (tonnes CO2 per kWp installed) is the difference between
the greenhouse gases saved and the greenhouse gases emitted over the life cycle of an
electricity-generating technology. It is calculated as follows:
CO2 mitigation potential = GHGs saved – GHGs emitted
One previous estimate of the CO2 mitigation potential of solar PV was found in a
technical report by the IEA PV Power Systems Programme (PVPS). This document
estimates the CO2 mitigation potential of multi-Si modules in 41 OECD cities (IEA,
2006). The maximum value reported for rooftop systems is 40 tonnes CO2 kWp-1
for
Perth in Australia. The lowest reported value is 0.1 tonnes CO2 kWp-1
for Oslo in
Norway. These calculations take into account the irradiance and displaced supply mix
in the country of installation, but it is not made clear how and where they assume the
modules were produced.
22
The annual net CO2 balance is the difference between the greenhouse gases saved and
the greenhouse gases emitted from the PV industry over one year. There are no
previous estimates of the value of this metric, although similar-sounding metrics can be
found in the literature. The Solar Generation V report from the European Photovoltaics
Industry Association (EPIA) and Greenpeace estimates that the „annual CO2 savings‟ of
the PV industry were 6 Mt CO2 in 2007. However, this assessment is potentially
misleading as it does not use a life cycle approach – consequently these calculations
only consider the CO2 savings made and emissions from the production of solar PV
modules are ignored.
23
3 Methodology
3.1 System boundary
3.1.1 Components included
The model considers grid-connected, rooftop systems containing mono-Si, multi-Si,
ribbon-Si and CdTe modules. The system boundary used in the model was the same as
that used in previous LCA studies of solar PV by Fthenakis et al. (2008) and Alsema
and de Wild-Scholten (2006). As in these studies, the frame and BoS components are
included but the decommissioning stage is excluded due to a lack of reliable energy data
for decommissioning operations. Module recycling is likely to become important in the
future, and preliminary results from demonstration projects indicate that this could
significantly reduce the energy demand of the PV module production process (Refocus,
2009), but this issue is not addressed in this project.
3.1.2 Sources of GHG emissions
Reich et al. (2007) divide the GHG emissions from PV module production into direct
emissions and indirect emissions. Direct emissions are those that arise from the
industrial processes themselves, such as the release of CO2 from quartzite reduction,
while indirect emissions are those due to energy consumption during the production
process.
Figure 11 shows all sources of GHG emissions from PV module production. Only
indirect CO2 emissions were included in the model. Reich et al. show that CO2
emissions from quartzite reduction are negligible (~0.5 g CO2-eq kWh-1
) and emissions
of CF4 during wafer etching are small and difficult to measure, so these were both
excluded from the model.
24
Figure 11 Sources of GHG emissions from solar PV module production. Sources not
included in the model are coloured grey.
3.1.3 Life cycle inventory data
The production energy data for crystalline silicon PV modules used in the model comes
from a life cycle inventory published by de Wild-Scholten and Alsema (2006) as part of
the EU-funded CrystalClear project. The dataset was created using a combination of
academic literature and data collected from 12 different Western European
manufacturers, which was averaged to protect sensitive commercial information. This
GHG emissions
Direct GHG emissions
CO2 from quartzite
reduction
CF4 from wafer etching
Indirect GHG emissions from
energy use
CO2 emissions from energy use
Process energy
Electricity
(from grid)
Electricity
(on-site)
Heat
(on-site)
Materials production
Silicon carbide
Glass
EVA
Aluminium and steel
Other materials
Transportation
Ship
Truck
Other GHG emissions from
energy use
25
life cycle inventory reflects the status of crystalline silicon technology in 2005-06. The
life cycle inventory data for CdTe modules is taken from Fthenakis and Kim (2006).
3.2 Model equations
This section introduces the equations used in the model. Detailed derivations of these
equations are provided in Appendix 4. A complete list of the parameter symbols used
in the equations is provided at the start of this report on page ii.
3.2.1 Levelised CO2 emissions
Levelised CO2 emissions (g CO2 kWh-1
) = output Lifetime
emissions CO Capital 2
Capital CO2 emissions = thth e1
td
e CEC E
Lifetime output = L PR I m
The technology type determines the quantity of electricity used in production (Ee), the
quantity of heat used in production (Eth), the module efficiency (ηm) and the module
lifetime (L). The performance ratio (PR) is a derating factor which accounts for factors
such as partial shading of the module area, snow cover and heat loss.
The carbon intensity of electricity generation (Ce1) depends on the generation mix in the
country/countries of production, while the carbon intensity of heat generation (Cth)
depends on the heat source used (usually natural gas). The irradiance (I) depends on the
climate in the country of installation.
The efficiency of the transmission and distribution network (ηtd) is included because the
electricity consumption data is for electricity at the factory gate, after transmission and
distribution losses, while the carbon intensity figures are for electricity generated,
before transmission and distribution losses.
(Equation 1)
(Equation 2)
26
3.2.2 Levelised CO2 emissions from transportation
Levelised GHG emissions (g CO2 kWh-1
) = output Lifetime
ation transportfrom emissions CO2
=
L PR I
Cd Cd m
m
trucktruckshipship
The distances travelled by ship and by truck (dship and dtruck) are the total distances
travelled by these modes of transport during the life cycle. The values used in the
model for the carbon intensity of transportation by ship and by truck (Cship and Ctruck, in
g CO2 kg-1
km-1
) were taken from Krauter and Ruther (2004). The mass per square
meter of module (m) is included to convert the units of the numerator from g CO2 kg-1
to g CO2 m-2
.
3.2.3 CO2 mitigation potential
The following equations for the CO2 mitigation potential apply to individual PV
systems. The term „individual‟ is used to reiterate that only emissions from this single
module are considered – emissions from the production of other modules due to the
growth of the PV industry are not included in this analysis.
CO2 mitigation potential (tonnes CO2 kWp-1
) = CO2 saved over life cycle – CO2 emitted over life cycle
CO2 saved over life cycle = 6
td
e2
10
C L PR I
CO2 emitted over life cycle = 6
m
ththe1
td
e
10
C EC E
(Equation 3)
(Equation 4)
(Equation 5)
27
The CO2 saved is proportional to the carbon intensity of the supply mix which the
output from the PV module is displacing in the country of installation (Ce2). The output
from the PV module is divided by the transmission and distribution network efficiency
(ηtd) because the module is displacing demand at the final user level. A factor of
6
m 10
1 is used to convert the units from g CO2 m-2
to tonnes CO2 kWp-1
.
The limiting conditions required to achieve a positive CO2 mitigation potential are
found by setting CO2 saved = CO2 emitted:
td
e2C =
L PR I
C E C E
m
ththe1
td
e
Or, equivalently:
td
e2C= Levelised CO2 emissions
3.2.4 Annual net CO2 balance
The annual net CO2 balance is an extension of the CO2 mitigation potential that
considers the CO2 emissions of the PV industry as a whole (i.e. a large number of
individual PV systems), taking into account the rate of growth of installed capacity. Its
design is inspired by the methodology of Lysen and Daey Ouwens (2002), who describe
a way to determine the annual net energy balance of the PV industry (see Appendix 4
for details).
The annual net CO2 balance is defined as follows:
Annual net CO2 balance (tonnes CO2 yr-1
) = CO2 saved by cumulative installed capacity that year – CO2
emitted from production of new installed capacity that year
(Equation 7)
(Equation 6)
28
CO2 saved by cumulative installed capacity = 6
td
e2
10
C* PR *I* GIC
CO2 emitted from production of new installed capacity = r *GIC*6
m
ththe1
td
e
10
C EC E
Where GIC is the cumulative installed capacity at the start of the year and r is the
annual rate of growth of installed capacity.
The limiting conditions required to achieve a positive annual net CO2 balance are:
r = ththtde1e
e2m
C EC E
C PR I
3.3 Limitations of the model
The model assumes that all modules produced in one year do not begin to save CO2
until the next calendar year, whereas in reality modules are generally installed a month
or so after they are produced.
It is assumed that the proportion of solar PV in the electricity generation mix remains
low over the next 10-20 years. For this reason the model is not iterative and does not
reward a high rate of industry growth one year with a decrease in national carbon
intensity the next year.
The model does not account for the effects of national PV market „saturation‟. Market
saturation occurs because in reality there is a limit to the total capacity that may be
installed in any one country – this limit may either be economic (due to high installation
costs, for example), political (such as the market cap in Spain of 500 MWp for 2009), or
technical (such as limits to the proportion of intermittent renewables that may be
(Equation 9)
(Equation 8)
(Equation 10)
29
accommodated by the grid). In most countries economic and political limits are much
more likely to be reached before technical limits. Once any one of these limits has been
reached the national PV market becomes „saturated‟ and PV module suppliers must
look elsewhere to sell their modules. If the PV market becomes saturated in a sunny
country with high carbon intensity due to an economic or a political barrier, rather than
due to a technical barrier, then this could significantly reduce the extent to which the
global PV industry achieves its potential for climate change mitigation. This would be
an interesting topic for further study.
30
4 Results and Analysis
4.1 Levelised CO2 emissions
4.1.1 Breakdown of levelised GHG emissions
Figure 12 and Figure 13 show breakdowns of the levelised CO2 emissions from
crystalline silicon and CdTe modules manufactured using the average Western Europe
grid mix (480 g CO2 kWh-1
, also known as the „UTCE‟ mix) and installed in Southern
European irradiance conditions.
Figure 12 Breakdown of levelised GHG emissions for crystalline silicon technologies
produced in Western Europe (carbon intensity 480 g CO2 kWh-1
) and installed in
Southern Europe (irradiance 1,700 kWh m-2
yr-1
).
For ribbon-Si and multi-Si modules, the largest source of CO2 emissions is energy use
during the poly-Si production stage. For mono-Si modules, the largest source is energy
use during the crystal-growing process in the wafer production stage. The multi-Si
wafers on which this production data is based are thinner than mono-Si wafers (240 μm
compared to 270 μm). Counter-intuitively, thin wafers currently require more poly-Si
per square meter than thicker ones due to greater sawing losses, despite the fact that
31
more wafers are recovered per ingot (de Wild-Scholten and Alsema, 2006). This is why
the CO2 emissions from poly-Si production for multi-Si modules are slightly higher
than those for mono-Si modules.
Figure 13 Breakdown of levelised GHG emissions for CdTe modules produced in
Western Europe (carbon intensity 480 g CO2 kWh-1
) and installed in Southern Europe
(irradiance 1,700 kWh m-2
yr-1
).
4.1.2 Module efficiency and quantity of electricity used in production
Together the module and the quantity of energy used in production can be used to
characterise the technology type (assuming that all technology types have a 30-year
lifetime and a performance ratio of 0.75). Figure 14 shows how levelised GHG
emissions are affected by these two parameters. Indications of where the different
technology types lie are shown on the diagram. Reductions in levelised CO2 emissions
can be achieved by reducing electricity consumption during production and increasing
module efficiency. The parameters shown for future crystalline silicon technologies are
based on improvements in the following areas (Alsema, 2000):
32
Increased module efficiency
Decreased wafer thickness
Reduced losses from wafer sawing
Use of FBR poly-Si and upgraded MG-Si feedstock
Improved casting methods such as electromagnetic casting
Figure 14 Sensitivity of levelised GHG emissions to module efficiency and quantity
of electricity used in production, for modules produced in Western Europe (carbon
intensity 480 g CO2 kWh-1
) and installed in Southern Europe (irradiance 1,700 kWh m-2
yr-1
).
4.1.3 Production and installation in different countries
The levelised GHG emissions depend on the carbon intensity of electricity generation in
the country of production and the irradiance in the country of installation. A summary
of these quantities in different countries is provided in Figure 15 and a table of the
values used is provided in Annex 1. The data used here is for 2007 and is taken from
the Carbon Monitoring for Action (CARMA, 2008) database. This source was chosen
33
for its broad coverage, which includes data for non-OECD countries such as China and
India. However, it should be noted that some discrepancies over national carbon
intensity values exist in the literature – for example, an annex in an IEA PVPS report
(IEA, 2006) quotes the carbon intensity in Japan as 508 g CO2 kWh-1
, while in the
CARMA database it is quoted as 365 g CO2 kWh-1
. These discrepancies mean that
some of the labels on the following diagrams could be misplaced, but they do not affect
the key findings of this report.
Figure 16 shows how the levelised GHG emissions of PV modules depend on where
they are produced and where they are installed. For modules produced in countries with
low-carbon generation mixes (such as France or Norway) and installed in countries with
high irradiance levels (such as Australia), the levelised GHG emissions are 6 - 9 g CO2
kWh-1
for crystalline silicon technologies and < 4 g CO2 kWh-1
for CdTe. For modules
produced in coal-burning nations (such as China) and installed in countries with low
irradiance levels (such as Germany or the UK), the levelised GHG emissions are 100 -
140 g CO2 kWh-1
for crystalline silicon technologies and 60 g CO2 kWh-1
for CdTe.
These two scenarios represent the two extremes.
Note that these scenarios assume that all stages of the supply chain are located in one
country – in reality different stages of the production process are often carried out in
different countries with different carbon intensities.
34
Figure 15 Carbon intensity of electricity generation and irradiance in different countries (CARMA, 2008; Energie-Atlas, 2005a; 2005b).
35
Figure 16 Sensitivity of levelised CO2 emissions of (a) ribbon-Si, (b) multi-Si, (c) mono-Si, and (d) CdTe modules to carbon intensity of
electricity production in the country of production and irradiance in the country of installation.
36
4.1.4 Transportation
Figure 17 shows levelised CO2 emissions from the transportation of crystalline silicon
and CdTe modules. The CO2 emissions from the shipping of PV modules are negligible,
even over large distances. The CO2 emissions from transportation by truck account for
almost all of the CO2 emissions from transportation.
Figure 17 shows that the method of transportation is a much more important factor than
the distance transported in determining the CO2 emissions from transportation.
Consequently, exporting PV modules from China to Germany does not necessarily emit
more CO2 than installing them within China – in fact the latter scenario may even cause
greater CO2 emissions if the modules have to be transported large distances internally
by truck.
For crystalline silicon the levelised emissions from transportation are typically in the
range 0-6 g CO2 kWh-1
. For widely dispersed supply chains involving multiple
journeys by truck between production stages, the CO2 emissions could be greater. In
the future the emissions per km of transportation are likely to decrease in many
countries as tighter fuel consumption standards for trucks are introduced. A possible
rebound effect from this might be that it becomes cheaper to transport materials long
distances by truck due to lower fuel consumption costs. If, on the other hand, fuel
prices rise significantly over the next decade or two then this would be expected to have
the opposite effect and encourage manufacturers to reduce transportation distances
during production and to locate several stages of the supply chain in the same country if
possible.
Despite requiring much lower volumes of semiconductor material, CdTe modules
currently weigh around twice as much as crystalline silicon modules because a thicker
layer of glass is used for encapsulation (Fthenakis et al., 2008). This, combined with
the lower efficiency of CdTe modules, results in levelised transportation emissions of
between 0-14 g CO2 kWh-1
– over twice those of crystalline silicon modules transported
the same distance. This means that the inclusion of transportation in the system
boundary narrows the advantage of CdTe over crystalline silicon in terms of levelised
37
CO2 emissions.
Figure 17 Levelised CO2 emissions from transportation of 1 m2 of (a) crystalline
silicon, and (b) CdTe modules (irradiance 1,700 kWh m-2
yr-1
).
38
Summary of Key Findings
The levelised CO2 emissions of CdTe modules are roughly half those of
crystalline silicon modules, under the same conditions.
For crystalline silicon modules the largest sources of CO2 emissions are the
poly-Si production stage and the crystal-growing stage (for mono-Si).
The levelised CO2 emissions of solar PV vary between 2-200 g CO2 kWh-1
for crystalline silicon modules and 1-100 g CO2 kWh-1
for CdTe, depending
on the country of production and the country of installation.
Levelised CO2 emissions from transportation are around 0-6 g CO2 kWh-1
for
crystalline silicon and twice as much for CdTe.
The mode of transport is more important than the distance travelled when
assessing CO2 emissions from transportation.
39
4.2 CO2 mitigation potential
4.2.1 CO2 saved and CO2 emitted
Figure 18 demonstrates how the function (CO2 saved – CO2 emitted) is derived from the
cumulative values for the CO2 saved and CO2 emitted over the lifetime of a PV module.
The term „CO2 saved‟ here does not mean that the PV module actively reduces the
atmospheric concentration of CO2, but refers to the CO2 emissions prevented that would
have otherwise occurred had the module not been deployed (due to the reduction of
output from an alternative electricity generation technology). The cumulative CO2
emitted appears as a flat horizontal line on the graph because all the life cycle CO2
emissions occur during production at t=0.
Figure 18 Cumulative CO2 savings, cumulative CO2 emissions and the function (CO2
saved – CO2 emitted) over the lifetime of a multi-Si module (Ce1 = Ce2 = 480 g CO2
kWh-1
, I = 1,700 kWh m-2
yr-1
). Positive values of y indicate CO2 savings, negative
values of y indicate CO2 emissions.
Figure 19 shows how the function (CO2 saved – CO2 emitted) varies for different
technology types. The CO2 payback time, defined as the number of years it takes to
save an amount of CO2 equal to that emitted during production, is the point at which
40
this function passes through y=0. CdTe modules have the shortest CO2 payback time,
currently around one year for modules installed in a sunny location (1,700 kWh m-2
yr-1
)
and displacing the average Western Europe grid mix, while mono-Si modules have the
longest CO2 payback time, at around three years under the same conditions.
The CO2 mitigation potential is the value of this function at t=30. The gradient of the
graph represents the annual CO2 mitigation potential (tonnes CO2 kWp-1
yr-1
). Figure 19
shows that the technology type has a very small impact on the CO2 mitigation potential
per kWp of solar PV modules (assuming that all technologies have a 30-year lifetime
and PR 0.75). Under the conditions shown, CdTe modules have the greatest CO2
mitigation potential at 19.1 tonnes CO2 kWp-1
while mono-Si modules have the lowest
potential at 18.1 tonnes CO2 kWp-1
. Note that a 1 kWp CdTe PV system has a greater
module area than a 1 kWp crystalline silicon PV system due to its lower conversion
efficiency.
Figure 19 The function (CO2 saved – CO2 emitted) for individual PV systems with a
30-year lifetime (Ce = 480 g CO2 kWh-1
, Cdg = 480 g CO2 kWh-1
, I = 1,700 kWh m-2
yr-
1).
41
4.2.2 Location of production and location of installation
Figure 20 shows how the CO2 mitigation potential of an individual crystalline silicon
PV system varies for different combinations of countries of production and installation.
There are three parameters in the equation for CO2 mitigation potential (Equation 3) that
are location-dependent – the carbon intensity of electricity used in production, the
irradiance and the carbon intensity of the displaced supply mix. The carbon intensity of
electricity used in production depends on the country of production, while the irradiance
and the carbon intensity of the displaced supply mix depend on the country of
installation.
The greatest CO2 mitigation potential is 45 tonnes CO2 kWp-1
for a module installed in
Australia (bar A), which has both high irradiance and a high proportion of coal in the
current supply mix. There is reasonable correlation with the estimate of 40 tonnes CO2
kWp-1
for Perth by the IEA (2006). The lowest mitigation potential occurs if a solar PV
module is used to displace other low-carbon technologies, such as hydro or nuclear.
The worst case scenario is production in China and installation in Norway (bar E),
which results in a negative CO2 mitigation potential of -3 tonnes CO2 kWp-1
.
A comparison of bars B, C and D in Figure 20 shows that the country of installation is a
much more important factor than the country of production in determining the CO2
mitigation potential of a 1 kWp system. When the country of installation is kept
constant and the country of production is changed (bars B and C), it makes very little
difference to the CO2 mitigation potential, but when the country of production is kept
constant and the country of installation is changed (bars B and D), the CO2 mitigation
potential is reduced by a factor of two.
This can be explained by considering the CO2 mitigation potential from an energy
payback point of view. The CO2 saved is the lifetime energy output from the module
multiplied by the carbon intensity of the displaced supply mix. The CO2 emitted is the
energy used in production multiplied by the carbon intensity of the production supply
mix. For modern PV modules the lifetime energy output is typically over ten times
42
greater than the energy used in production. Therefore the calculation of the CO2
mitigation potential essentially involves taking a large number (the CO2 saved) and
subtracting from it a small number (the CO2 emitted). Doubling the value of the small
number (by doubling the carbon intensity of the production supply mix) has less of an
impact on the result than doubling the large number (by doubling the irradiance or
doubling the carbon intensity of the displaced supply mix).
This is also the reason why the CO2 mitigation potential per kWp varies so little between
technology types, if they are assumed to have the same module lifetime and
performance ratio (Figure 19). The module efficiency and quantity of electricity used in
production affect the magnitude of CO2 emitted and therefore have little impact on the
CO2 mitigation potential. The module lifetime and performance ratio, on the other hand,
affect the magnitude of CO2 saved and have a large impact on the CO2 mitigation
potential. A summary of parameters and which term they affect is shown in Table 5.
Table 5 Parameters used in CO2 mitigation potential calculation
CO2 saved CO2 emitted
Irradiance (I)
Carbon intensity of displaced supply mix (Ce2)
Performance ratio (PR)
Module lifetime (L)
Quantity of electricity used in production (Ee)
Carbon intensity of electricity used in production (Ce1)
Module efficiency (ηm)
43
Figure 20 CO2 mitigation potential of an individual crystalline silicon module with a 30-year lifetime for different combinations of
countries of production and installation.
Levelised CO2 emissions / g CO2 kWh-1
Irradiance / kWh m-2 yr-1
Displaced supply mix / g CO2 kWh-1
Production supply mix / g CO2 kWh-1
Installation
Production
44
The levelised CO2 emissions are also shown at the bottom of Figure 20. This is to
demonstrate that levelised CO2 emissions are a poor indicator of the climate change
mitigation potential of solar PV. This is because they do not account for the carbon
intensity of the displaced supply mix. For example, the levelised CO2 emissions for a
crystalline silicon PV module produced and installed in China are relatively high, at 89
g CO2 kWh-1
, while the levelised CO2 emissions for a module produced and installed in
Japan are half as much, at 45 g CO2 kWh-1
. Using this information alone, the Japanese
case appears more favourable. In fact quite the opposite is true: the CO2 mitigation
potential is three times greater in the Chinese case than the Japanese case due to the
higher carbon intensity of the displaced supply mix.
Levelised CO2 emissions in g CO2 kWh-1
have the advantage that they can be calculated
for all electricity-generating technologies (unlike an alternative such as g CO2 m-2
) and
allow for quick comparison between them. However, their value in a rigorous
discussion of the climate change mitigation potential of solar PV is limited. This issue
is not limited to solar PV; it applies to all renewables with a location-specific output,
such as wind, wave or tidal power.
4.2.3 Case study 1: PV modules produced in China
Figure 21 shows all possible values of the CO2 mitigation potential for a PV module
produced in China. The point at which CO2 Breakeven is reached is the intersection
between the blue and the red areas at the bottom of the chart. If modules are installed in
Brazil, France or Norway, the CO2 mitigation potential is negative due to the low
carbon intensity of the displaced supply mix in these countries. However, in all other
countries where the supply mix displaced is currently fossil fuel based, the CO2
mitigation potential is positive – even in countries of low irradiance such as Germany
and the UK.
In 2007, 98% of cells and modules produced in China were exported (EU JRC, 2008).
However, Figure 21 shows that should China decide to focus on the creation of a
domestic PV industry, rather than exporting its modules abroad, then this would have a
positive impact in terms of climate change mitigation because the CO2 mitigation
45
potential of Chinese modules installed in China is generally higher than for Chinese
modules installed in other countries. However, the reverse is true in the case of
modules produced in Japan; here greater CO2 savings are generally be made by
installing the modules abroad rather than domestically because the national carbon
intensity is relatively low.
46
Figure 21 CO2 mitigation potential for crystalline silicon PV modules produced in China and installed in different countries.
47
4.2.4 Case study 2: Displacing different components of the supply mix in Germany
The chart presented in Figure 21 assumes that the output from solar PV displaces the
national supply mix in each country of installation. It concludes, therefore, that the CO2
mitigation potential of solar PV modules installed in Germany is greater than those
installed in Japan because the higher carbon intensity of the displaced supply mix in
Germany outweighs the difference in climatic conditions. However, one might contest
this statement by pointing out that it is actually output from the marginal generator in
the supply mix that will be curtailed to make way for the output from solar PV, and this
affects the CO2 mitigation potential because the carbon intensity of the marginal
generator is different to that of the average supply mix.
Figure 22 shows what happens to the CO2 mitigation potential of a solar PV module
made in China and installed in Germany if its output displaces different components of
the supply mix. It shows that if a PV module in Germany displaces gas, rather than the
national supply mix, then its CO2 mitigation potential is reduced by 50% and becomes
lower than that of a module installed in Japan.
Figure 22 CO2 mitigation potential of a crystalline silicon module made in China and
used to displace coal, gas, the national supply mix, nuclear or renewables in Germany.
48
A further complication is caused by the fact that the carbon intensity of national supply
mixes can vary significantly by month, by day and even by hour. To give an example,
in the UK the carbon intensity of the national grid can be as low as 234 g CO2 kWh-1
in
the early hours of the morning during summer or as high as 664 g CO2 kWh-1
on a
weekday evening in winter (Earth Notes, 2009). This means it depends on both the
month and the time of day as to which marginal generator the output from solar PV is
displacing, and the carbon intensity of the displaced marginal generator is certainly not
fixed throughout the lifetime of the PV module. Further work is needed in this area to
clarify what exactly it is that is being displaced by solar PV in different countries, and to
improve how the carbon intensity of the displaced supply mix is treated in CO2
mitigation potential calculations.
Summary of Key Findings
The CO2 mitigation potential per kWp varies very little between different
technology types.
The CO2 mitigation potential of crystalline silicon modules varies between
–3 and 45 tonnes CO2 kWp-1
. The most important factors influencing its
value are the carbon intensity of the displaced supply mix and the irradiance.
Strictly speaking the carbon intensity of the marginal generator in the
displaced supply mix should be used in the CO2 mitigation potential
calculation rather than the displaced supply mix average.
Levelised CO2 emissions are a poor indicator of the potential of solar PV
modules to mitigate climate change.
49
4.3 Annual net CO2 balance
4.3.1 Difference between the annual net CO2 balance and the CO2 mitigation
potential
The results presented in the previous section were for individual PV systems, analysed
over their 30-year lifetime. In this case the CO2 saved term is generally much larger
than the CO2 emitted term. However, the results change if the net CO2 balance of the
global installed capacity is analysed over a time period of one year and the effects of
industry growth are taken into account. Now the CO2 saved term and the CO2 emitted
term are generally much closer in magnitude, so parameters affecting the CO2 emitted
term have a much more significant impact on the annual net CO2 balance than they do
on the CO2 mitigation potential. A summary of differences between the CO2 mitigation
potential and the annual net CO2 balance is shown in Table 6.
Table 6 Differences between the CO2 mitigation potential and the annual net CO2
balance
CO2 mitigation potential Annual net CO2 balance
Unit tonnes CO2 kWp-1
tonnes CO2 yr-1
or tonnes CO2 kWp-1
yr-1
Applies to: Single 1 kWp module The global installed capacity
Time period analysed 30 years 1 year
Relative magnitude of terms CO2 saved >> CO2 emitted CO2 saved ≈ CO2 emitted
Accounts for industry growth No Yes
4.3.2 Location of production and location of installation
Figure 23 shows the annual net CO2 balance of the PV industry for different
combinations of countries of production and installation. The units of the y-axis are
tonnes CO2 kWp-1
yr-1
. To calculate the annual net CO2 balance of the industry in
tonnes CO2 yr-1
this value is multiplied by the global installed capacity (kWp).
The case of 0% growth shows what would happen if production of new PV modules
50
were to cease altogether. When multiplied by 30 this gives the „static‟ CO2 mitigation
potential results shown previously in Figure 20 (the values in fact vary slightly because
in Figure 23 it is assumed that the production emissions from the cumulative installed
capacity occurred in a previous year, but the ranking order is the same).
When industry growth is introduced, the annual net CO2 balance becomes less
favourable because the CO2 savings made by the cumulative installed capacity are
cancelled out to some extent by the CO2 emissions from the production of new installed
capacity. As the rate of industry growth is increased, the „erosion‟ of the CO2 savings
made due to the production of new installed capacity is greater, and in some cases the
annual net CO2 balance becomes negative.
Cases A, B, D and E demonstrate the dramatic impact of high industry growth for
modules produced in China. Due to the high carbon intensity in China, a high industry
rate very rapidly erodes the CO2 savings made and results in a low or negative annual
net CO2 balance. At 40% growth, the annual net CO2 balance is negative in all cases for
PV modules produced in China.
Case C is a close approximation to the present situation, as a significant proportion of
the global installed capacity is currently both made and installed in Germany. In case C,
growth rates of 23% or above result in a negative annual net CO2 balance. It was
previously shown that the PV industry had an average annual growth rate of over 40%
between 1998 and 2008 (Mehta and Bradford, 2009). Therefore the annual net CO2
balance was almost certainly negative in these years, and this in turn means that the
cumulative net CO2 balance of the PV industry is currently negative.
This should not be interpreted as an argument against the use of solar PV for climate
change mitigation. The point to be made here is that in order to achieve significant CO2
savings in the longer term it will be necessary to accept that the net CO2 balance of the
industry may be low or even negative during the early stages of industry expansion.
Due to our widespread dependence on fossil fuels to power nearly all modern
production processes this is an unavoidable feature of the transition to a low-carbon
economy.
51
Figure 23 Annual net CO2 balance (in tonnes kWp-1
yr-1
) of the crystalline silicon PV industry at different growth rates for different
combinations of countries of production and installation.
Irradiance / kWh m-2 yr-1
Displaced supply mix / g CO2 kWh-1
Production supply mix / g CO2 kWh-1
Installation
Production
C A B D E
52
4.3.3 Industry growth and decarbonisation
Figure 24 shows projections for the global solar PV installed capacity between 2010 and
2030 from the IEA Blue scenario (IEA, 2008). The IEA predict that PV industry
growth will slow down over the next 15 years but then pick up again after 2025.
Figure 24 IEA Blue scenario for global solar PV installed capacity 2010-2030 (IEA,
2008).
The weighted average irradiance and weighted average displaced carbon intensity of the
cumulative installed capacity in 2008 were 1,200 kWh m-2
yr-1
and 500 g CO2 kWh-1
respectively (see Figure 4). Figure 25 shows what will happen to the annual net CO2
balance in the future if the this distribution of installed capacity remains the same and
the industry grows as predicted by the IEA Blue scenario shown above. For each year,
the annual net CO2 balance is shown at five different values of production carbon
intensity.
53
Figure 25 Annual net CO2 balance of the solar PV industry 2007-2030, with different values for the carbon intensity used in production.
Industry growth rates and installed capacities beyond 2008 are projections from the IEA Blue scenario (IEA, 2008). The upper chart shows
the annual net CO2 balance in units of tonnes CO2 kWp-1
yr-1
, the lower graph shows it in Mt CO2 yr-1
.
Industry growth rate
Installed capacity / GWp
Irradiance / kWh m-2 yr-1
Displaced carbon intensity / g CO2 kWh-1
Year
54
In 2007 and 2008 the annual net CO2 balance was almost certainly negative. Given that
the four leading producers in the PV module supply chain - Germany, Japan, the USA
and China – have an average carbon intensity between them of around 600 g CO2 kWh-1
it is very unlikely that the average carbon intensity of production was below 500 g CO2
kWh-1
in these years. A comparison of the red 500 g CO2 kWh-1
bar for 2007 in Figure
25 with the green 30% industry growth bar in case C of Figure 23 shows reasonable
agreement, indicating that the net CO2 balance in 2007 was around -0.1 tonnes CO2
kWp-1
, or –0.8 Mt CO2 in total. This is markedly different from the claim in the EPIA
and Greenpeace report (2008) that 6 Mt CO2 were saved by the PV industry in 2007,
and this demonstrates the importance of using a life cycle approach for such
calculations.
The rate of industry growth of 61% in 2008 was exceptionally high. Assuming an
average production carbon intensity of 500 g CO2 kWh-1
, the net CO2 balance of the PV
industry in 2008 was –5 Mt CO2. However, lower industry growth rates of 20% or less
are predicted between 2010 and 2030, and this should result in a positive net CO2
balance in these years. This does not mean that industry growth itself is undesirable,
but rather that it must be balanced with decarbonisation of the electricity used in
production and installation of new installed capacity in sunny countries with high
carbon intensities.
The five different production carbon intensity scenarios shown for each year show the
impact of decarbonisation on the annual net CO2 balance. If the production process is
powered by renewables or nuclear with CO2 emissions of 100 g kWh-1
or less then the
annual net CO2 balance is positive in all cases – even if annual growth is 61%. By 2030
a PV industry powered by renewables or nuclear could have an annual net CO2 balance
of over 100 Mt CO2 yr-1
. This is good news for the PV industry, as it shows that solar
PV could indeed start to achieve significant CO2 savings in the near future. These
numbers present a strong case for pursuing rapid decarbonisation of the electricity
supply to production facilities.
The opposite is true if the production carbon intensity becomes higher, for example if
production shifts to Asian countries where manufacturing facilities are powered by
55
unabated coal-fired power stations. In this case the annual net CO2 balance is negative
for industry growth rates over 16% and in the worse case could reach -32 Mt CO2 yr-1
in
2030. It was shown previously that industry experts predict a rapid increase of PV
manufacturing capacity in China over the next few years, particularly for the energy-
intensive poly-Si production stage. If their predictions hold true, and if China‟s
factories continue to be powered by unabated coal, then this will hamper the transition
of the industry from a negative net CO2 balance to a positive one.
This model assumes that the technologies used and the geographical distribution of
installed capacity remains the same over the next 20 years – that is, crystalline silicon
modules installed mainly in Germany, Japan and the USA. In reality these factors will
evolve over time: production methods for crystalline silicon will become more efficient,
thin film technologies with lower production energy demands are likely to start being
produced on a large scale, and policy measures to support solar PV will be introduced in
new countries. If the weighted average irradiance or the weighted average displaced
carbon intensity increases in the future, due to the stimulation of new PV markets in
sunny and coal-based countries such as India, then this would improve the annual net
CO2 balance because the „CO2 saved‟ term is proportional to these two parameters. If,
on the other hand, these averages decrease in the future, due to the stimulation of a large
PV market in countries such as France, then this will have a negative impact on the
annual net CO2 balance and it will be longer before the industry achieves its potential to
cut CO2 emissions.
A factor not taken into account by this model is CO2 emitted due to the construction of
new PV module manufacturing facilities. As the cumulative installed capacity
approaches the terawatt scale and the annual production volumes become large, this
could become an increasingly important factor. It is unlikely that the CO2 emissions
from this source would follow a regular annual trend - production facilities are more
likely to be built in rounds when investment conditions are favourable, resulting in large
spikes of CO2 emissions every few years. The author knows of no previous studies that
have estimated CO2 emissions from new PV production facilities so this would be an
interesting area for further study.
56
Summary of Key Findings
The annual net CO2 balance is different to the CO2 mitigation potential
because it takes into account industry growth.
Higher rates of industry growth result in lower net CO2 balances, particularly
if the production carbon intensity is high.
Both the annual and cumulative net CO2 balance of the PV industry has
almost certainly been negative over the past 10 years. However, the
relatively low industry growth rates predicted for the next 10-20 years are
likely to result in positive annual net CO2 balances.
To increase the annual net CO2 balance of the PV industry, PV modules
should be manufactured using efficient production processes that use energy
from low-carbon sources and installed in sunny countries with high carbon
intensities.
57
5 Discussion
5.1 The importance of a life cycle approach
A comparison of the results from this project with the „annual CO2 savings‟ claimed by
the EPIA and Greenpeace (2008) shows the importance of using a life cycle approach
when dealing with CO2 emissions figures. By only considering the CO2 saved by the
PV industry and not the CO2 emitted by the production of new modules, the partial
analysis presented by the EPIA and Greenpeace gives an overly optimistic impression
of the current CO2 balance of the PV industry. If, on the other hand, life cycle studies
are based on out-of-date information then this can lead to overly pessimistic
impressions of the environmental performance of current PV technologies. These
points emphasise the importance of using high quality information to inform the debate
about technology options to mitigate climate change.
It is also important to select the appropriate life cycle metric, depending on the question
being asked. It has been shown, for example, that the levelised CO2 emissions are a
poor indicator of the value of solar PV for mitigating climate change, and that the CO2
mitigation potential or annual net CO2 balance are more appropriate life cycle metrics
for this purpose.
5.2 Technological solutions
5.2.1 Targets for emerging solar PV technologies
This work has clarified the influence of the quantity of electricity used in production
and the module efficiency on the levelised CO2 emissions of solar PV. The pace of
technological improvement in the PV industry is rapid – the energy efficiency of
production processes and module conversion efficiencies are being improved year on
year - and this means that LCA studies based on a specific set of input parameters very
quickly become out of date. The same is true to some extent for all technologies, but
particularly so in the case of solar PV due to the rapid industry expansion in recent
years and because a great deal of scope remains for further improvement of the
58
technology (unlike more mature technologies like wind where the scope for further
improvements in system performance are more limited).
This information can be used to set a target for emerging solar PV technologies such as
organic solar PV and other thin film technologies, if they aspire to achieve lower
levelised CO2 emissions than present crystalline silicon technologies. The overall
efficiency of any PV technology can be summarised by the ratio of its production
electricity demand to its module conversion efficiency (kWh input per unit module
efficiency). The value of this efficiency ratio for crystalline silicon technologies is
shown in Table 7.
Table 7 Ratios of production electricity demand to module efficiency
Technology type Ratio of production electricity demand to module efficiency
/ kWh per unit module conversion efficiency
Mono-Si 36
Multi-Si 31
Ribbon-Si 26
New technologies with an efficiency ratio of 26 kWh per unit module conversion
efficiency or below represent an improvement over current crystalline silicon
technologies. CdTe achieves this standard, with a ratio of around 16. Note, however,
that in the future the ratio for crystalline silicon technologies is expected to decrease
further to around 12 (see Figure 14, page 32), so emerging solar PV technologies will be
chasing a moving target.
5.2.2 Options for decarbonising process electricity
As well as decreasing the quantity of electricity used in production per unit module
efficiency obtained, another option for reducing the capital CO2 emissions from PV
module manufacture is to decarbonise the electricity used in production. There are three
ways in which this could be achieved. The first is to locate PV production facilities in
countries where the carbon intensity is already low. Energy-intensive industries tend to
be located where electricity prices are low – this can lead to their location in countries
with abundant renewable resources, such as Norway or Iceland, but low energy prices
59
can also exist in countries with abundant coal reserves. However, it would not be
practical to locate all manufacturing facilities in the small number of countries where
carbon intensity is already low.
The second option is to rapidly decarbonise the national supply mix in other countries.
Many countries, both developed and developing, now plan to increase the proportion of
renewables in their generation mix to address both climate change and energy security
concerns. To give one example, the UK hopes to achieve near complete
decarbonisation of the electricity generation sector by 2030 (Committee on Climate
Change, 2008). Changing the carbon intensity of the electricity mix has what Pehnt
(2006) would call an imported impact on LCA results, as it would improve not only the
climate change mitigation potential of solar PV but also all other electricity-generating
technologies.
The third option is to use on-site generation from renewables to meet a greater
proportion of the electricity demand of PV manufacturing facilities. Fthenakis et al.
(2008) explore the potential of using solar PV modules themselves for this purpose,
creating what they term a „PV Breeder‟. They estimate that using solar PV modules to
supply 30% of the electricity needs of production facilities could reduced levelised CO2
emissions by 10%, while the use of solar PV modules together with compressed air
storage systems could allow 100% of the electricity needs to be met by solar PV and
reduce emissions by up to 68%. The decarbonisation of heat generation by the
replacement of natural gas with biomass boilers could also reduce CO2 emissions,
although the main focus should be on electricity as this accounts for 96% of CO2
emissions from PV production (see Figure 12).
It may be that the production facilities for some electricity generation technologies may
be easier to power from onsite renewables than others, which would make
decarbonisation in this case an inherent rather than an imported impact. A comparison
of the prospects for decarbonising the production facilities of different clean energy
technologies using onsite renewables would be a useful area for further study.
60
5.3 Solar PV must not displace renewables or nuclear
It has been shown that the CO2 mitigation potential of PV modules is negative if their
output is used to displace other low-carbon electricity-generating technologies such as
nuclear or other renewables. At present, in most electricity markets, there is no
mechanism in place to prevent this from happening. In the UK, for example, while
there are various incentives such as the Renewables Obligation and the forthcoming
feed-in tariff that aim to encourage uptake of renewables, there is nothing in the
electricity trading arrangements that gives solar PV power plants priority access to the
grid over fossil fuel power plants. At the current low levels of penetration of
renewables this does not yet cause a problem, but at higher penetrations it may become
an important issue.
In the future there could potentially be conflict between decentralised domestic and
utility-scale PV systems in European countries. There is currently much interest in the
possibility of building large solar power stations in the Sahara desert and exporting the
electricity to Europe via long-distance High Voltage Direct Current (HVDC) lines. If
this proposal were to become a reality, then at times of high irradiance during summer -
just as utility-scale PV power plants in the desert are coming online - the output from
domestic PV systems will increase and demand for imported grid electricity will be
reduced. In this scenario, decentralised PV modules are effectively displacing output
from PV power plants and this would reduce the CO2 mitigation potential of both PV
systems. This point strengthens the case for developing energy storage systems for use
in utility-scale PV power plants in deserts to smooth their output profile.
The same applies to other renewables, in particular wind and nuclear as these are both
used to meet baseload demand. If wind is used to displace nuclear or vice versa then
this will reduce the CO2 mitigation potential of these technologies. In addition, if a
national grid mix contains a very high proportion of wind then the output from wind
farms may occasionally need to be „dumped‟ at times when conditions are windy but
demand is low. From a life cycle perspective this has a negative impact on the CO2
mitigation potential of wind farms as again the output is not being used to displace
fossil fuels. The point to be made here is that policies must be developed to ensure that
61
the output from renewable generators such as solar PV is not used to displace the output
from other low-carbon sources once penetration levels of low-carbon technologies
become significant.
5.4 Bringing together economic and environmental objectives
5.4.1 Investing money and carbon into solar PV
It is necessary to invest both money and carbon in the development of renewable energy
sources such as solar PV now in order to make them cost-competitive with conventional
energy sources in the longer term. Money must be invested because production
processes are still relatively small in scale and expensive compared to those used in
incumbent energy industries, while carbon must be emitted because production facilities
are still largely powered by fossil fuels at present.
Eventually a return on these investments should be expected. Stimulating the market
causes it to expand, leading to cost reductions. Market expansion currently causes CO2
emissions from the production of new solar PV modules, but under the right conditions
it will eventually reduce CO2 emissions from the energy sector. The difficult task is
drawing the line and deciding how much money and carbon we can afford to „spend‟
now in order to receive greater returns in the long term.
PV industry trends are currently determined mainly by economics. Some of the current
trends being driven by price signals are also beneficial for climate change mitigation –
such as improvement of the energy efficiency of production processes to reduce
manufacturing costs – while others conflict with climate change mitigation goals - such
as the building of new production facilities powered by unabated coal in Asia. The
findings in this report support the view that new financial mechanisms are needed to
ensure that the investment decisions made by international PV production companies
take into account both economic and environmental factors such as climate change
mitigation.
In the future it is possible that some major PV exporting nations could shift their focus
62
back to domestic markets, perhaps in order to meet renewable energy targets or to
stimulate the domestic job market. The implications of such shifts for climate change
mitigation would be country-specific – for example, it has been shown that if Chinese
PV modules are installed in China rather than exported then this would generally have a
positive impact, while the opposite would be true in Germany or Japan. Emissions from
transportation are case specific and the findings in this report suggest that there is no
guarantee that a reduction in the distances over which modules are traded worldwide
would result in a reduction of CO2 emissions from transportation.
5.4.2 International collaboration is needed
At present there is a general mismatch between the best places to install solar PV from a
CO2 mitigation point of view and the location of financial capacity needed to stimulate
the PV industry. This is neatly illustrated by the fact that of the top ten nations
spending public money on solar PV research and development (R&D) in 2007, eight
have a solar PV CO2 mitigation potential of under 12 tonnes CO2 kWp-1
and three have a
negative potential, as shown in Figure 26.
Figure 26 Top ten public budgets for solar PV R&D in 2007 and the CO2 mitigation
63
potential of solar PV in these countries (IEA, 2008).
Taxpayers and electricity consumers in countries such as Germany and Japan are
currently stimulating the PV market, expanding it rapidly and driving down costs.
Eventually these cost reductions will lead to the penetration of PV into new markets in
poorer countries with good solar resources. The problem is the urgency of the climate
change issue - if solar PV is to play a significant role in climate change mitigation then
the pace of this transition may need to be accelerated, and a high level of international
collaboration will be needed to achieve this.
One potential solution is for governments in countries such as Germany and Japan to
provide financial stimulus packages to expand solar PV markets in poorer countries
where the CO2 mitigation potential of the solar PV modules is higher and the cost per
tonne of CO2 abated is lower. Such transfers of wealth from rich to poor countries are
likely to be a defining feature of future international efforts to both mitigate and adapt to
climate change. The forthcoming UNFCCC meeting at Copenhagen in December 2009
would be a good place at which to discuss the creation of mechanisms to ensure in the
future that solar PV modules are installed in the most effective places.
It is not just national governments that can take action. Individuals can also play a role
in ensuring that solar PV modules are installed in countries with the highest CO2
mitigation potentials. For example, instead of installing a domestic solar PV system on
their own roof, individuals in Germany and the UK who are willing to spend their
disposable income on climate change mitigation could fund the installation of a solar
PV system in another country, where it will have a greater impact.
The sooner international action is taken to decarbonise electricity supplies and improve
the distribution of solar PV installed capacity, the sooner the PV industry will make the
transition from being a net emitter to a net saver of CO2. While its long-term potential
to cut greenhouse gas emissions from the electricity generation sector is large, solar PV
cannot achieve this difficult task alone and simultaneous efforts must be sustained to
accelerate development of other low-carbon energy technologies alongside solar PV.
64
6 Conclusions
This report found that levelised CO2 emissions for solar PV are in the range 2-200 g
CO2 kWh-1
depending on the technology type, the country of production and the
country of installation. Levelised CO2 emissions are roughly proportional to the carbon
intensity of the supply mix used in production and inversely proportional to irradiance.
Crystalline silicon modules manufactured in Western Europe using low-carbon supply
mixes and installed in Southern Europe have levelised CO2 emissions of around 40-50 g
CO2 kWh-1
. The levelised CO2 emissions of CdTe modules are around half those of
crystalline silicon modules under the same conditions. These results agree well with
previous estimates in the literature. Transportation, which is not included in most
previous studies, is estimated to add an additional 0-6 g CO2 kWh-1
for crystalline
silicon modules and 0-12 g CO2 kWh-1
for CdTe modules. Although the levelised CO2
emissions of renewable energy technologies are frequently quoted in the literature, their
value in discussions of climate change mitigation potential is limited because they are
unaffected by the carbon intensity of the displaced supply mix.
The CO2 mitigation potential is more valuable because it does take into account the
displaced supply mix. The CO2 mitigation potential of crystalline silicon technologies
is in the range -3 to 45 tonnes CO2 kWp-1
. The energy generated over the lifetime of a
solar PV module is over ten times greater than the energy consumed during its
manufacture, which means that the irradiance and the carbon intensity of the displaced
supply mix are much more important factors in determining the CO2 mitigation
potential than the supply mix used in production or the technology type. The CO2
mitigation potential of PV modules installed in Australia (45 tonnes CO2 kWp-1
) is three
times greater than PV modules installed in Germany (12 tonnes CO2 kWp-1
). If PV
modules are used to displace nuclear or renewables then the CO2 mitigation potential is
negative. Calculating the displaced carbon intensity during the lifetime of a solar PV
module is complicated because the carbon intensity of grid mixes varies by month and
by hour.
The annual net CO2 balance is a new metric that takes into account both the carbon
intensity of the displaced supply mix and industry growth. It calculates the CO2 saved
65
by the total global installed capacity in one year and subtracts from it the CO2 emitted
from the production of new modules that year. This is the first time that the link
between industry growth and CO2 emissions has been examined. The results show that
high levels of industry growth can dramatically erode the CO2 savings made over the
course of a year, and the record levels of PV industry growth in recent years have
resulting in negative annual net CO2 balances. It is estimated that the annual net CO2
balance was -0.8 Mt CO2 in 2007 and -5 Mt CO2 in 2008 (assuming 500 g CO2 kWh-1
as the average carbon intensity of electricity used in production). The cumulative net
CO2 balance of the PV industry is presently negative, but could turn positive over the
next 10-20 years as lower industry growth rates give the CO2 balance time to recover.
The net CO2 balance of the PV industry could be further improved by decreasing the
quantity of electricity used in production, increasing module efficiencies, using low-
carbon sources of energy for PV module production and increasing the number of PV
modules installed in sunny countries with high carbon intensities.
Underlying the present distribution of installed capacity is a mismatch between the
location of financial resources and the best places to install solar PV from a climate
change mitigation perspective. A high level of international collaboration will be
needed to address this challenge and create policies to ensure that the PV industry (1)
continues to attract investment, and (2) achieves its potential to mitigate climate change.
6.1.1 Recommendations for Further Research
Calculate the annual net CO2 balance of other low-carbon energy industries
Assess the impact of module recycling on LCA results
Investigate the effects of PV market saturation
Assess CO2 emissions from the construction of new solar PV production
faculties
Compare the prospects for using onsite renewables to power the production
facilities of different low-carbon energy technologies
Improve the method for estimating the carbon intensity of the supply mix
displaced by renewable generators
66
References
Alsema, E. A. (2000) Energy pay-back time and CO2 emissions of PV systems. Progress in
Photovoltaics, 8 (1), 17-25.
Alsema, E. A. & Nieuwlaar, E. (2000) Energy viability of photovoltaic systems. Energy Policy, 28 (14),
999-1010.
Alsema, E. A. & De Wild-Scholten, M. J. (2006) Environmental Impacts of Crystalline Silicon
Photovoltaic Module Production. Presented at 13th CIRP International Conference on Life Cycle
Engineering, Leuven, 31 May - 2 June 2006. [Online] Available from:
http://www.ecn.nl/docs/library/report/2006/rx06041.pdf [Accessed 11 June 2009].
Alsema, E. A., De Wild-Scholten, M. J. & Fthenakis, V. M. (2006) Environmental Impacts of PV
Electricity Generation - A Critical Comparison of Energy Supply Options. Paper presented at the 21st
European Photovoltaic Solar Energy Conference, Dresden, 4-8 September 2006. Available from:
http://www.ecn.nl/docs/library/report/2006/rx06016.pdf [Accessed 19 May 2009].
Australian Coal Association. (2001) Coal in a sustainable society. Australian Coal Industry Research
Programme Report.
BERR. (2008) Fuel Mix Disclosure Data Table. [Online] Available from:
http://www.berr.gov.uk/energy/markets/electricity-markets/fuel-mix/page21629.html [Accessed 10 June
2009].
Boyle, G. (ed.) (2004) Renewable Energy. Oxford, Oxford University Press.
Bradford, T. (2008) Polysilicon: Supply, Demand, & Implications for the PV Industry. Report produced
by GreenTech Media Inc. and the Prometheus Institute for Sustainable Development. [Online] Available
from: http://www.gtmresearch.com/report/polysilicon-supply-demand-and-implications-for-the-pv-
industry [Accessed 23 July 2009].
Braga, A. F. B., Moreira, S. P., Zampieri, P. R., Bacchin, J. M. G., Mei, P. R. (2008) New processes for
the production of solar-grade polycrystalline silicon: A review. Solar Energy Materials and Solar Cells,
92 (4), 418-424.
Carbon Trust. (2008) Greenhouse Gas Conversion Factors. [Online] Available from:
http://www.carbontrust.co.uk/resource/conversion_factors/default.htm [Accessed 19 June 2009].
CARMA. (2007) Carbon Monitoring for Action. [Online] Available from: http://carma.org/ [Accessed 5
August 2009].
Central Research Institute of Electric Power Industry (CRIEPI). (1995) Energy Technology Life Cycle
Analysis that Takes CO2 Emission Reduction Into Consideration. Annual Research Report. [Online]
Available from:
http://www.webcitation.org/query?url=http%3A%2F%2Fcriepi.denken.or.jp%2Fen%2Fe_publication%2
Fa1995%2Fseika95kankyo23E.html&date=2008-11-01 [Accessed 14 August 2009].
Committee on Climate Change. (2008) Building a Low-Carbon Economy - the UK's contribution to
tackling climate change. TSO: London. Available from: http://www.theccc.org.uk/reports/building-a-low-
carbon-economy [Accessed 14 July 2009].
CrystalClear. (2009) CrystalClear Project Summary. Available from:
http://www.ipcrystalclear.info/Paginas/FinalEvent.aspx [Accessed 16 June 2009].
De Wild-Scholten, M. J. & Alsema, E. A. (2005) Environmental Life Cycle Inventory of Crystalline
Silicon Photovoltaic Module Production. Presented at Materials Research Society Fall 2005 Meeting,
67
November 2005, Boston, USA. [Online] Available from:
http://www.ecn.nl/docs/library/report/2006/rx06005.pdf [Accessed 11 June 2009].
De Wild-Scholten, M. J. & Alsema, E. A. (2006) Environmental Life Cycle Inventory of Crystalline
Silicon Photovoltaic Module Production (Excel spreadsheet). [Online] Available from:
http://www.ecn.nl/docs/library/report/2006/c06002-LCI_data-cSiPV-pub-v1.xls [Accessed 11 June
2009].
De Wild-Scholten, M. J. & Alsema, E. A. (2007) Environmental Life Cycle Inventory of Crystalline
Silicon Photovoltaic System Production: status 2005/2006 (Excel spreadsheet). [Online] Available from:
http://www.ecn.nl/docs/library/report/2007/e07026-LCIdata-cSiPV-pubv2_0.xls [Accessed 11 June
2009].
Distances.com. (2009) Flying distances between 325 major airports in the World. [Online] Available
from: http://www.distances.com/distance_fly.php [Accessed 5 August 2009].
Dones, R., Heck, T. & Kirschberg, S. (2003) Greenhouse Gas Emissions From Energy Systems:
Comparison and Overview. Paul Scherrer Institut Annual Report 2003, Annex IV. [Online] Available
from: http://gabe.web.psi.ch/pdfs/Annex_IV_Dones_et_al_2003.pdf [Accessed 5 May 2009] .
Earth Notes. (2009) A Note On Variations in UK Grid Electricity CO2 Intensity with Time. [Online]
Available from: http://www.earth.org.uk/note-on-UK-grid-CO2-intensity-variations.html [Accessed 6
September 2009].
Energie-Atlas. (2005a) Yearly solar radiation in North and South America. [Online] Available at:
http://www.helpsavetheclimate.com/solar.html [Accessed 5 August 2009].
Energie-Atlas. (2005b) Yearly solar radiation in Europe, Africa, Asia and Oceania. [Online] Available
at: http://www.energie-atlas.ch/schools-freemaps-1-2-2.htm [Accessed 5 August 2009].
EPIA. (2009) Global Market Outlook for Photovoltaics Until 2013. Available from:
http://www.epia.org/publications/epia-publications.html [Accessed 23 July 2009].
EPIA & Greenpeace. (2008) Solar Generation V – 2008. [Online] Available from:
http://www.epia.org/fileadmin/EPIA_docs/documents/EPIA_SG_V_ENGLISH_FULL_Sept2008.pdf
[Accessed 28 April 2009].
EU PV Platform. (2007) The Status of the PV Industry. [Online] Available from:
http://www.eupvplatform.org/index.php?id=133 [Accessed 22 August 2009].
European Commission. (2003) External Costs: Research results on socio-environmental damages due to
electricity and transport. Office for Official Publications of the European Communities, Luxembourg.
[Online] Available from: http://www.externe.info/externpr.pdf [Accessed 27 May 2009].
European Commission. (2008) Energy Sources, Production Costs and Performance of Technologies for
Power Generation, Heating and Transport. Commission Staff Working Document accompanying the
Communication From the Commission To the European Parliament, the Council, the European Economic
and Social Committee and the Committee of the Regions, Commission of the European Communities.
[Online] Available from: http://eur-
lex.europa.eu/LexUriServ/LexUriServ.do?uri=SEC:2008:2872:FIN:EN:PDF [Accessed 14 August 2009].
European Commission Joint Research Centre (EC JRC). (2008) PV Status Report 2008. [Online]
Available from: http://re.jrc.ec.europa.eu/refsys/html/Recent%20Publications.htm [Accessed 24 July
2009].
ExternE. (1997) ExternE National Implementation Germany. Externalities of Energy European
Commission Research Project. [Online] Available from:
http://www.webcitation.org/query?url=http%3A%2F%2Fweb.archive.org%2Fweb%2F20061007113502
%2Fexterne.jrc.es%2Fger.pdf&date=2008-10-30 [Accessed 14 August 2009].
68
First Solar. (2009) Fast Facts: Company Overview. [Online] Available from:
http://www.firstsolar.com/pdf/FS_Company_FastFacts_MD-5-601-NA.pdf [Accessed 1 September
2009].
Flynn, H. & Bradford, T. (2006) Polysilicon: Supply, Demand, & Implications for the PV Industry.
Report produced by the Prometheus Institute for Sustainable Development.
Frankl, P., Masini, A., Gamberale, M. & Toccaceli, D. (1998) Simplified life-cycle analysis of PV
systems in buildings: Present situation and future trends. Progress in Photovoltaics, 6 (2), 137-146.
Frankl, P., Menichetti, E. & Raugei, M. (2005) Deliverable No. 11.2 - RS Ia: Final report on technical
data, costs and life cycle inventories of PV applications. Report from the European Commission's
NEEDS Project. Available from: http://www.needs-
project.org/docs/results/RS1a/RS1a%20D11.2%20Final%20report%20on%20PV%20technology.pdf
[Accessed 18 May 2009].
Fthenakis, V. M. & Alsema, E. A. (2006) Photovoltaics energy payback times, greenhouse gas emissions
and external costs: 2004 - early 2005 status. Progress in Photovoltaics, 14 (3), 275-280.
Fthenakis, V. M. & Kim, H. C. (2007) Greenhouse-gas emissions from solar electric- and nuclear power:
A life-cycle study. Energy Policy, 35 (4), 2549-2557.
Fthenakis, V. M., Kim, H. C. & Alsema, E. A. (2008) Emissions from Photovoltaic Life Cycles. Environ.
Sci. Technol., 42 (6), 2168-2174.
Hondo, H. (2009) Life cycle GHG emission analysis of power generation systems: Japanese case. Energy,
30 (11-12), 2042-2056.
IAEA. (2000) Greenhouse Gas Emissions Of Electricity Generation Chains: Assessing the Difference.
IAEA Bulletin 42/2/2000. [Online] Available from:
http://www.webcitation.org/query?url=http%3A%2F%2Fwww.iaea.org%2FPublications%2FMagazines
%2FBulletin%2FBull422%2Farticle4.pdf&date=2008-11-01 [Accessed 14 August 2009].
IEA. (2006) Compared assessment of selected environmental indicators of photovoltaic electricity in
OECD cities. Report from the IEA Photovoltaic Power Systems (PVPS) Task 10. [Online] Available
from: http://www.iea-pvps-task10.org/rubrique.php3?id_rubrique=4 [Accessed 12 August 2009].
IEA. (2008) Energy Technologies Perspectives 2008 – Scenarios and Strategies to 2050. ISBN 978-92-
64-04142-4.
IEA. (2009) National Survey Report of PV Power Applications in the United Kingdom 2008. Available
from: http://www.iea-pvps.org/countries/uk/index.htm [Accessed 19 May 2009].
ISO 14040. (2006) Environmental management - Life cycle assessment -Principles and framework.
International Organisation for Standardisation (ISO), Geneva.
Jungbluth, N., Tuchschmid, M. & De Wild-Scholten, M. (2008) Life Cycle Assessment of Photovoltaics:
Update of ecoinvent data v2.0. Working paper. Available from: http://www.esu-
services.ch/cms/fileadmin/download/jungbluth-2008-LCA-PV-web.pdf [Accessed 29 May 2009].
Krauter, S. & Ruther, R. (2004) Considerations for the calculation of greenhouse gas reduction by
photovoltaic solar energy. Renewable Energy, 29 (3), 345-355.
Lysen, E. & Daey Ouwens, C. (2002) Energy effects of the rapid growth of PV capacity and the urgent
need to reduce the energy payback time. Paper presented at the PV in Europe, From PV Technology to
Energy Solutions Conference, Rome, 7-11 October 2002 .
Mehta, S. & Bradford, T. (2009) PV Technology, Production and Cost, 2009 Forecast: The Anatomy of a
Shakeout. Report produced by GreenTech Media Inc. and the Prometheus Institute for Sustainable
69
Development. [Online] Available from: http://www.gtmresearch.com/report/pv-technology-production-
and-cost-2009-forecast [Accessed 23 July 2009].
Nemet, G. F. (2006) Beyond the learning curve: factors influencing cost reductions in photovoltaics.
Energy Policy, 34 (17), 3218-3232.
Pacca, S., Sivaraman, D. & Keoleian, G. A. (2007) Parameters affecting the life cycle performance of PV
technologies and systems. Energy Policy, 35 (6), 3316-3326.
Parliamentary Office of Science and Technology (POST). (2006) Carbon Footprint of Electricity
Generation. Available from: http://www.parliament.uk/documents/upload/postpn268.pdf [Accessed 10
June 2009].
Pearce, J. M. (2008) Limitations of Greenhouse Gas Mitigation Technologies Set By Rapid Growth and
Energy Cannibalism. Paper presented at the Climate 2008 Conference. Available from:
http://www.climate2008.net/?a1=pap&cat=1&e=61 [Accessed 21 May 2009].
Pehnt, M. (2006) Dynamic life cycle assessment (LCA) of renewable energy technologies. Renewable
Energy, 31 (1), 55-71.
Raugei, M., Frankl, P., Alsema, E. A., De Wild-Scholten, M. J., Fthenakis, V. M. & Kim, H. C. (2007)
Life Cycle Assessment of Present and Future Photovoltaic Systems. Presentation given at the
'Expectations and Advanced Technologies in Renewable Energy' AIST Symposium, Chiba, Japan, 11
October 2007.
Refocus. (2009) End-of-life PV: then what? - Recycling solar PV panels. 3 August 2009. [Online]
Available from: http://www.renewableenergyfocus.com/view/3005/endoflife-pv-then-what-recycling-
solar-pv-panels/ [Accessed 13 August 2009, requires registration].
Reich, N. H., Alsema, E. A., van Sark, W. G. J. H. M. & Nieuwlaar, E. (2007) CO2 Emissions of PV in
the Perspective of a Renewable Energy Economy. Paper presented at the 22nd European Photovoltaic
Solar Energy Conference, Milan, 3-7 September 2007. [Online] Available from: http://igitur-
archive.library.uu.nl/chem/2008-0506-201407/UUindex.html [Accessed 15 May 2009] .
Reich-Weiser, C., Dornfeld, D. A. & Horne, S. (2008b) Environmental Assessment and Metrics for Solar:
Case Study of SolFocus Solar Concentrator Systems. [Online] Available from:
http://www.me.berkeley.edu/~corinne/IEEE_PV_May_2008.pdf [Accessed 13 May 2009].
Theodoropoulos. K. (2009) MSc Sustainable Energy Futures, Imperial College London. (Personal
Communication, 27th
August 2009).
Timeanddate.com. (2009) Distance Calculator. Available from:
http://www.timeanddate.com/worldclock/distance.html [Accessed 23 July 2009].
Vattenfall. (1999) Vattenfall’s Life Cycle Studies of Electricity. [Online] Available from:
http://www.webcitation.org/query?url=http%3A%2F%2Fbarsebackkraft.se%2Ffiles%2Flifecycle_studies
.pdf&date=2008-11-01 [Accessed 14 August 2009].
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Appendix 1 - Carbon intensity and irradiance by country
Table 8 Carbon intensity of electricity generation and irradiance in different countries,
in order of decreasing carbon intensity (CARMA, 2008; Energie-Atlas, 2005a; 2005b)
Country Carbon intensity of electricity
generation (2007) / g CO2 kWh-1
Irradiance / kWh m-2
yr-1
Poland 1002 1000
Australia 891 2100
China 868 1500
India 805 2000
Czech Republic 742 1100
Indonesia 662 1700
Germany 612 1000
USA 611 1700
Taiwan 570 1500
UK 557 1000
Portugal 550 1700
Spain 487 1700
Russia 484 900
South Korea 444 1300
Italy 429 1500
Japan 365 1300
Belgium 317 1000
Canada 213 1200
France 88 1300
Brazil 50 1800
Norway 5 800
71
Appendix 2 - Model parameters
Table 9 Model parameters
Parameter Symbol Ribbon-Si Multi-Si Mono-Si CdTe Unit
Carbon intensity of heat generation Cth 185 185 185 185 g CO2 kWh-1
Carbon intensity of transportation by ship Cship 0.001 0.001 0.001 0.001 g CO2 kg-1 km-1
Carbon intensity of transportation by truck Ctruck 0.35 0.35 0.35 0.35 g CO2 kg-1 km-1
Quantity of electricity used in production (all
stages) Ee 296 407 498 143 kWhfinal m
-2
Quantity of heat used in production Eth 38 65 59 0 kWhprimary m-2
Module lifetime L 30 30 30 30 yrs
Mass per square meter of module m 46 46 46 92 kg m-2
Performance Ratio PR 0.75 0.75 0.75 0.75 -
Module efficiency ηm 11.5% 13.2% 14.0% 9.0% -
Efficiency of transmission and distribution
network ηtd 0.92 0.92 0.92 0.92 -
Table 10 Emissions factors for electricity and heat generation (BERR, 2008; Carbon
Trust, 2008). The values for electricity-generating technologies refer to operational
emissions, not life cycle emissions.
Energy Source g CO2 kWhe-1
g CO2 kWhth-
1
Coal 910 -
Natural gas 360 185
Nuclear 0 -
Renewables 0 -
Other 610 -
72
73
Appendix 3 - Derivations of model equations
Equation 4
CO2 saved over life cycle = Lifetime output * Carbon intensity of displaced supply mix
* conversion factor (to convert from g CO2 m-2
to tonnes CO2 kWp-1
)
CO2 saved over life cycle =
6
m
e2
td
m
10
1*C*
L PR I
CO2 saved over life cycle = 6
td
e2
10
C L PR I
Equation 5
CO2 emitted over life cycle = Capital CO2 emissions * conversion factor
CO2 emitted over life cycle =
6
m
ththe1
td
e
10
1C EC
E
CO2 emitted over life cycle = 6
m
ththe1
td
e
10
C EC E
Equation 6
The limiting conditions required to achieve a positive CO2 mitigation potential are
found by setting CO2 saved = CO2 emitted:
74
6
td
e2
10
C L PR I
=
6
m
ththe1
td
e
10
C EC E
which is rearranged to give:
td
e2C =
L PR I
C E C E
m
ththe1
td
e
Equation 7
Substituting the equation for levelised CO2 emissions (Equations 1 and 2) into the above
gives:
td
e2C = Levelised CO2 emissions
Equations 8 - 10
In any one year the CO2 (in tonnes) saved by the global installed capacity (GIC, in kWp)
is:
CO2 saved that year = GIC * CO2 saved per kWp per year
CO2 saved = 6
td
e2
10
C* PR *I* GIC
Assume that in the same year the industry produces a further r * GIC of modules, where
r is the rate of growth of global installed capacity. The CO2 emitted by the production
of these new modules is:
75
CO2 emitted that year = r * GIC * capital CO2 emissions per kWp
CO2 emitted = r *GIC*6
m
ththe1
td
e
10
C EC E
These equations can be divided by GIC to convert from tonnes CO2 yr-1
to tonnes CO2
kWp-1
yr-1
, i.e. to give a number that is independent of the initial installed capacity.
The limiting conditions required to achieve a positive annual net CO2 balance are:
CO2 saved = CO2 emitted
6
td
e2
10
C* PR *I* GIC
= r *GIC*
6
m
ththe1
td
e
10
C EC E
r = ththtde1e
e2m
C EC E
C PR I