Nuclear Power Carbon Emissions: A Life Cycle Analysis

Applied Energy 118 (2014) 68–82
Contents lists available at ScienceDirect
Applied Energy
journal homepage: www.elsevier.com/locate/apenergy
Life cycle analysis on carbon emissions from power
generation – The nuclear energy example
Victor Nian a,⇑, S.K. Chou a, Bin Su a, John Bauly a
a
Energy Studies Institute, National University of Singapore, Singapore
h i g h l i g h t s
This paper discusses about a methodology on the life cycle analysis of power generation using nuclear as an example.
The methodology encompasses generic system, input–output, and boundaries definitions.
The boundaries facilitate the use of Kaya Identity and decomposition technique to identify carbon emission streams.
a r t i c l e
i n f o
Article history:
Received 18 July 2013
Received in revised form 6 December 2013
Accepted 16 December 2013
Available online 7 January 2014
Keywords:
Life cycle analysis
Carbon emission
System boundary
Nuclear
a b s t r a c t
A common value of carbon emission factor, t-CO2/GWh, in nuclear power generation reported in the literature varies by more than a factor of 100. Such a variation suggests a margin of uncertainty and reliability. In this study, we employ a bottom-up approach to better define the system, its input and
output, and boundaries. This approach offers improved granularity at the process level and consistency
in the results. Based on this approach, we have developed a methodology to enable comparison of carbon
emissions from nuclear power generation. The proposed methodology employs the principle of energy
balance on a defined power generation system. The resulting system boundary facilitates the use of
the ‘‘Kaya Identity’’ and the decomposition technique to identify the carbon emission streams. Using
nuclear power as a case study, we obtained a carbon emission factor of 22.80 t-CO2/GWh, which falls
to within 2.5% of the median of globally reported LCA results. We demonstrate that the resulting
methodology could be used as a generic tool for life cycle analysis of carbon emissions from other power
generation technologies and systems.
Ó 2013 Elsevier Ltd. All rights reserved.
1. Introduction
There is a strong scientific consensus that continued rising
trend of global warming caused by anthropogenic Greenhouse
Gas (GHG) emissions may lead to catastrophic climate change,
threatening life of millions [1,2]. Climate change will have severe
adverse effect on the sustainable development and poverty eradication efforts globally and particularly to the Southeast Asian region [3]. It is therefore important to take on urgent action in
decarbonizing our energy systems. According to the United Nations Framework Convention on Climate Change (UNFCCC), the
‘‘environmentally sound technologies’’ are central to mitigating climate change by reducing GHG emissions. In the context of energy
technologies, these include fission, solar, wind, hydro, and other
renewable energy technologies.
⇑ Corresponding author. Tel.: +65 66012076.
E-mail address: [email protected] (V. Nian).
0306-2619/$ - see front matter Ó 2013 Elsevier Ltd. All rights reserved.
http://dx.doi.org/10.1016/j.apenergy.2013.12.015
The life cycle analysis (LCA) is the most popular methodology in
evaluating the ‘‘soundness’’ of energy technologies. With power
generation accounting for nearly half of the global GHG emissions,
there is an increasing number of LCA studies on alternative power
generation technologies. Fission power (nuclear) has emerged as
one of the most popular subject [4]. Fission power is special because it has strategic importance in addressing not just climate
change, but also energy security [5]. In the ‘‘450 Scenario’’ developed by the International Energy Agency (IEA), fission power
would contribute to 6% carbon emissions from the BusinessAs-Usual (BAU) scenario [6].
The ISO 14040:2006 [7] has documented a general framework
for an LCA. It is a general guideline for an LCA exercise on products,
but it cannot be directly applied to power generation. In the literature, there is a wealthy pool of discussions on the LCA on power
generation, fission power in particular [8–19]. A general approach
for an LCA on GHG emissions of fission power includes the
accounting from uranium mining, to milling, to refining, to spent
fuel storage, and final geological storage (Fig. 1). Based on the same
V. Nian et al. / Applied Energy 118 (2014) 68–82
69
Nomenclature
Abbreviations
BAU
Business-As-Usual
GDP
Gross Domestic Product
GHG
Greenhouse Gas
GWh
Gigawatt hour
IAEA
International Atomic Energy Agency
IEA
International Energy Agency
IPCC
Intergovernmental Panel on Climate Change
LCA
Life Cycle Analysis
LWR
Light Water Reactor
MACC
Marginal Abatement Cost Curve
MWD
Megawatt Day
PCA
Process Chain Analysis
SWU
Separative Work Unit
TWh
Terawatt hour
UNFCCC United Nations Framework Convention on Climate
Change
Symbols
An
the nth process of the ‘‘LCA Sub-system’’ on the ‘‘Energy
Input’’ side
an
‘‘product’’ of ‘‘Process’’ An
Bn
the nth process of the ‘‘LCA Sub-system’’ on the ‘‘NonEnergy Input’’ side
bn
‘‘product’’ of ‘‘Process’’ Bn
C
carbon (GHG) emissions
CExt
extrinsic emission of ‘‘Process’’ input
CInt
intrinsic emission of ‘‘Process’’ input
CFuel
carbon emissions from the ‘‘Fuel’’
CLC
life cycle carbon emissions from the ‘‘LCA Main System’’
Csys
carbon emissions of a technological system
CE
carbon emissions due to ‘‘Energy Input’’
CE,n
carbon emissions from ‘‘Process’’ (Pn) activities due to
‘‘Energy Input’’
CE-sub
carbon emissions due to ‘‘Energy Input’’ at the immediate ‘‘Sub-system’’
Cf
carbon emissions from fuel fabrication
Cm
carbon emissions from the mining
CNE
carbon emissions due to ‘‘Non-Energy Input’’
CNE,n
carbon emissions from ‘‘Process’’ (Pn) activities due to
‘‘Non-Energy Input’’
CNE-sub
carbon emissions due to ‘‘Non-Energy Input’’ at the
immediate ‘‘Sub-system’’
Cp
carbon emissions from power generation
c
carbon intensity or emission factor
approach, several methodologies were developed specifically for
fission power with corresponding life cycle GHG emission factors.
However, the reported life cycle GHG emission factors varied by
more than a factor 100 in the literature (Fig. 2). Therefore, it seems
to suggest that the values and their associated methodologies may
not all be reliable.
In turn, these reliability issues may have severe consequences
as many globally recognized studies are heavily relying on the
LCA results from the literature. Pacala and Socolow [20] developed
a methodology under which replacing conventional fossil fueled
technologies with new and clean technologies could form wedges
that would help stabilize the concentration of CO2 in the atmosphere. As mentioned earlier, the LCA results on power generation
are relevant to the development the IEA’s 450 Scenario. In the
discussion on future emission scenarios, the IPCC [21] employed
ce,i
ce,i
cExt
cint
csys
Ef
Ef-in
Ef-loss
Ei,j,or k
Ein
Em-in
Em-loss
En
Eloss
Eout
Ep-in
e
ei
Fuel
Memit
Mf-res
Mp-res
MGHG
Mres
memit
NEG
NEin
NEn
NEi,j
ne
nei
Pn
Pe
pn
T
e
gsys
s
/
or k
carbon intensity of ‘‘Energy Input’’
carbon intensity of ‘‘Non-Energy Input’’
extrinsic emission factor of Process input
intrinsic emission factor of Process input
carbon emissions factor of a technological system
the amount of energy or heat released from ‘‘Fuel’’
energy input to the fuel fabrication system
energy loss of the fuel fabrication system
‘‘Energy Input’’ to each ‘‘Process’’ of the ‘‘LCA Main
System’’ or ‘‘Sub-system’’
‘‘Energy Input’’ to a system
energy input to the mining system
energy loss of the mining system
‘‘Energy Input’’ to support the ‘‘Process’’ activities
energy loss of a system
energy output from a system
energy input to the power generation system
energy intensity of product from a life cycle Process
energy intensity of ‘‘product’’ pn by type
the substance required to produce thermal energy
mass of the emissions from a system
mass of wastes (residues) from the fuel fabrication
system
wastes (residues) from the power generation system
GHG emissions from power generation
wastes (residues) produced from power generation
emission factor of a system
net energy gain
‘‘Non-Energy Input’’ (e.g. materials, facilities, etc.) to
power generation
‘‘Non-Energy Input’’ to support the ‘‘Process’’ activities
‘‘Non-Energy Input’’ to each ‘‘Process’’ of the ‘‘LCA Main
System’’ or ‘‘Sub-system’’
intensity of ‘‘Non-Energy Input’’ consumption of
‘‘product’’ in a ‘‘Process’’
intensity of ‘‘Non-Energy Input’’ required to ‘‘produce’’ pn
by type
the nth ‘‘Process’’ of the ‘‘LCA Main System’’
generating capacity of the plant
product of the nth Process of the ‘‘LCA Main System’’
lifetime of the model plant (years)
energy payback time
(overall) system energy efficiency
dimensionless energy payback time
loading factor
LCA results from the literature in benchmarking the emissions
from alternative power generation technologies with key policy
recommendations. These LCA results were also used by the consultants at Mckinsey for the development of a global Marginal Abatement Cost Curve (MACC) [22,23]. At the regional level, the ADB
employed LCA results in the discussion about climate change in
Southeast Asia [3]. At the country level, the U.S. Energy Information
Administration employed LCA results for the agency’s Annual Energy Outlook series [24]. All of these authoritative findings will become unreliable with the use of unreliable LCA results. As such, it
may lead to the wrong decision making, which could result in
adverse effects in decarbonizing the energy systems.
In brief, there are three problems with the LCA methodologies
on power generation in the literature. First of all, there is a lack
of standardized LCA methodology on power generation. As such,
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V. Nian et al. / Applied Energy 118 (2014) 68–82
Fig. 1. General framework of an LCA on fission power generation.
there is no common platform to benchmark the LCA results. Secondly, majority of the current methodologies are technology specific, which encumbers benchmarking among alternative power
generation technologies. Lastly and most importantly, there is a
lack of generic system representation on power generation technology. An LCA methodology on solar PV cannot be directly applied
to fission power without significant adjustments. Furthermore,
there is no methodology capable of benchmarking fission power
with a complex power generation system, such as a hybrid diesel-backed solar PV system. The root cause to these problems lies
with three ‘‘non-generic’’ definitions: (1) non-generic system definition, (2) non-generic input–output definition, and (3) non-generic system boundaries definitions.
To systematically address the root cause, we propose a standardized methodology in this paper. In brief, the methodology is
built upon the first principle and the decomposition technique.
Key advantages of our methodology are: (1) the ability of evaluate
and benchmark the life cycle carbon emission factors of alternative
power generation systems, (2) balanced and unbiased energyemission analysis with accurate inclusion of emission streams,
(3) evaluation of key indicators of an energy system, such as energy
payback and (overall) system energy efficiency, and (4) the versatility of being applied to other aspects of an LCA, such as total
waste generation and total natural resource consumption.
This paper comprises of 6 sections. Section 1 is the introduction.
In Section 2, we start with a literature survey using a scorecard
method. In Section 3, we establish the proposed LCA methodology.
In Section 4, we employ a reference light water reactor (LWR) plant
as a numerical case study to validate the proposed methodology. In
Section 5, we discuss about the benefits and limitations of the
proposed methodology. In Section 6, we conclude the paper with
recommendations for future work.
2. Literature review
In the literature, there are broadly two approaches in conducing
LCA exercises, the top down and the bottom up approaches. In the
top down approach, the input–output (IO) method has emerged as
Fig. 2. Life cycle emissions of fission power generation reported in the literature
(logarithm plot).
the most popular method. The IO method enables a standardized
platform for scholars to evaluate energy systems in a multiregional
setting [25,26], with the ability to estimate the feedback effects
[27] among the different systems. It is a robust method in evaluating energy systems at the macroeconomic level. However, it is
lacking in granularity at the engineering process level. The bottom
up approach enables high granularity at this level. Although there
is Process Chain Analysis (PCA) [15] in the bottom up approach,
there is a lack of standardized methodology. As such, this paper
focuses on the bottom up approach.
There are four main criteria used in our literature review: LCA
framework, LCA Scope, LCA System, and LCA Inventory Data. It is
understood that these four criteria are important for an LCA to
be accurate and objective. To enhance the granularity of our
evaluation, each of the four main criteria was further divided into
sub-criteria. Under the ‘‘LCA Framework’’, we examine whether the
current LCA frameworks are generic enough to represent all power
generation technologies; whether the methodology is capable of
analyzing different aspects of the an LCA, such as GHG emissions,
waste generation, system energy efficiency); and whether the
LCA can accurately account for the emission streams. Under this
criterion, there is a significantly lack of standardization among
the published methodologies. Each published work represented a
unique life cycle system even though they claimed to have been
based on the same approach. There is lack of transparency in
accounting for the emission streams, such as [10,19,28].
Under the ‘‘LCA Scope’’, we examine whether the current LCA
methodologies degree of focus in conducting a cradle-to-grave
analysis; whether the scopes can be systematically expanded to include more emission streams; and whether there is identifiable
timeframe. Under this criterion, most of the published works seem
to have diluted the scope for assessment with the most common
dilution between the study on GHG emissions and waste stream
generation, such as [10]. There is also ambiguity in the timeframe
of assessment. Such ambiguity refers to the lack of distinction between the lifetime of a power plant and a complete uranium fuel
cycle, such as [29,30].
Under the ‘‘LCA System’’, we examine whether the system and
its input–output definitions are sufficiently generic to represent
all possible power generation technologies; benchmark on the
V. Nian et al. / Applied Energy 118 (2014) 68–82
granularity of the PCA used in the methodologies; critically examine the system boundaries defined in each methodology; and
benchmark on the completeness of a cradle-to-grave life cycle definition. Under this criterion, there is a lack of a generic system and
its associated input–output definitions, such as [12,13]; and there
is little attention paid to the system boundaries, such as
[9,11,15,31]. More critically, majority of the current methodologies
are technology specific. An LCA for fission power cannot be directly
apply to solar PV for benchmarking. As such, it is difficult to
identify a common platform to benchmark the life cycle emission
factors of alternative power generation technologies.
Under the ‘‘LCA Inventory Data’’, we examine whether there are
issues with the accessibility of the reference data; benchmark the
granularity of the data used in the analysis; and examine whether
the published methodology is based on the first principle. Under
this criterion, there are issues with the accessibility of data, such
as language barrier and proprietary restrictions. Many of the reference data are lacking in granularity, which greatly increase the difficulty in validation. In most instances, there is a lack of
transparent analysis on fuel-to-fuel, and fuel-to-energy conversion
but heavy reliance on secondary sources with only process or
system emission factors, such as [14,32].
Pertaining to the system boundaries, Storm van Leeuwen [33]
has clearly pointed out that ‘‘Many controversies with regard to fission power turn out to originate applying different system boundaries and time horizon: which processes and activities are included
in the system and which are not. Often the system boundaries are
not explicitly defined’’. Warner and Heath [30] developed a systematic view and harmonization of LCA on fission power generation. The objective of [30] was to determine the causes of
variability in estimating the life cycle GHG emissions from fission
power so as to reduce such variations. The harmonization has
established consistent gross system boundaries and key values
for system parameters. However, the transparency and granularity
of the methodology can be further improved. More specifically, detailed analysis on why and how process inputs are included and/or
excluded should be further discussed in the analysis.
The root cause to these problems lies with the ‘‘Non-generic’’
definitions on the life cycle system, input–output, and system
boundaries. To systematically address the root cause, the proposed
methodology is built upon both quantitative and qualitative analysis. In defining the generic life cycle system, we follow closely the
well-established PCA and Net-Energy Analysis. In addition, the
quantifiable metrics for fuel-to-fuel and fuel-to-energy conversion
from the first principle are added to the formulation. In defining
the system boundaries, we employ the popular decomposition
technique to reveal generic system boundaries.
3. The methodology
3.1. Defining a generic power generation system
71
Fig. 3. Generic definition of power generation system.
Power generation typically produces more than one type of
environmentally harmful byproducts such as emissions (airborne
pollutants and GHGs) and wastes (Fig. 4). If it were coal, the combustion of ‘‘Fuel’’ would result in emissions, Memit (largely CO2 and
airborne pollutants), and waste residues, Mres (e.g. coal ash). We
could express the environmental emissions via Eq. (3) for system
emission factor, memit.
memit ¼ Memit =Eout ðt-Emission=GWhÞ
ð3Þ
In the case of solar PV or wind technology, Ein comes from the carrier excitation energy from photons (solar radiation) or the kinetic
energy of moving air molecules. These processes do not involve
the chemical conversion of ‘‘Fuel’’. As such, there is no release of airborne emissions (Memit = 0), or waste streams (Mres = 0) from the
‘‘Fuel’’. Thus, these technologies were classified as ‘‘clean’’
technologies.
Applying Eq. (3) specifically for carbon (collectively representing all GHGs) emission factor:
csys ¼ MGHG =Eout ðt-CO2 =GWhÞ
ð4Þ
To expand towards a life cycle system representation, we define
two generic systems for Fuel Fabrication and Mining of Natural Resources respectively. To ensure consistency in the input–output definitions and system boundaries, we define these two systems in a
similar approach. For Fuel Fabrication system, it comprises of energy input Ef-in, ‘‘Crude Energy’’ (in the form of natural resources,
such as coal mines, uranium ore, or crude oil, ‘‘Fuel’’ output, energy
losses in the fabrication process Ef-loss, carbon emissions Cf, and
waste residues Mf-res. The Mining system can be defined in the same
approach. For the power generation system, it comprises of energy
input Ein from the conversion of Fuel, energy (electricity) output Eout,
energy loss in the power generation process Ep-loss, carbon emissions
Cp, and waste residues Mp-res (Fig. 5).
In this case, the energy input, Ef-in and Em-in to the system refers
to the energy input required to build, operate and maintain, and
decommission the system with losses to the surroundings. Since
We define a generic power generation system by extending the
cyclic process representation [34] into a generic system representation. The generic power generation system receives energy input,
Ein to produce useful energy output, Eout, and energy losses to the
surroundings, Eloss, (Fig. 3). The energy input could come from
the thermal energy produced in a furnace or fission reactor, kinetic
energy from the flow of air or water molecules (wind or hydro), or
carrier excitation energy from photon (solar radiation).
Under this system boundary and input–output definitions, we
could express the energy balance with Eq. (1), and the system
energy efficiency with Eq. (2).
Eout ¼ Ein Eloss
ð1Þ
gsys ¼ Eout =Ein
ð2Þ
Fig. 4. Generic power generation system with representations on environmental
impact.
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V. Nian et al. / Applied Energy 118 (2014) 68–82
Fig. 5. Power generation and upstream fuel fabrication.
they are consistently defined, we merge the two systems into a
broader fuel fabrication system definition (Fig. 6).
In the power generation system, Ef (directly converted from
‘‘Fuel’’) appeared to be the energy input for producing electricity.
Comparing with the energy input definition of the Fuel Fabrication
systems, there is inconsistency in system and input–output definitions. Fig. 6 still assumes a simplified conversion process from Fuel
to electricity. There is a lack of emission streams from other activities, such as construction, operation and maintenance, and decommissioning activities.
In a classical mechanical engineering viewpoint, ‘‘Fuel’’ enters
the furnace or fission reactor to produce thermal energy (heat)
through chemical conversion or fission reaction; the thermal
energy (heat) is then used to raise steam (in the case of Rankine
Cycle) that drives a turbine generator to produce electricity (Eout);
the input ‘‘Fuel’’ then converts into airborne emissions (including
pollutants), Memit and/or waste streams Mres. Moving beyond the
classical thermodynamic principles towards a unified generic life
cycle system representation, we ‘‘internalize’’ the ‘‘Fuel’’ to energy
conversion process. At the same time, we introduce a new energy
input definition Ep-in to the power generation system (Fig. 7). It is
defined as the energy required to build, operate and maintain,
and decommission the power generation system. In Fig. 6, Ep-loss
was the energy loss from the ‘‘Fuel’’ to electricity conversion process alone, but in Fig. 7, Ep-loss refers to the aggregated energy loss
from the consumption of Ep-in and Ef at the system level.
Since the power generation system has been defined consistently, we merge the two system into a unified life cycle system
representation (Fig. 8). The internalization of ‘‘Crude Energy’’ to
‘‘Fuel’’ to Ef represents deterministic and finite internal energy
generation.
To complete the generic system representation, we add physical
structures (e.g. blades of a turbine generator, reactor core for
fission power, etc.) to the system representation in Fig. 8. We give
these physical building blocks a generic input definition, ‘‘Non-Energy Input’’, denoted by NEin (Fig. 9). To simplify the representation
of life cycle power generation process, we internalize the production of ‘‘Crude Energy’’ and ‘‘Fuel’’ to Ef conversion process. The final
life cycle power generation system is schematized in Fig. 10.
In the PCA approach, a life cycle system may comprise of n
number of ‘‘Processes’’. Each ‘‘Process’’ comes from a standalone
system, such as Fuel Fabrication system. Inputs to each ‘‘Process’’
are defined as generic ‘‘Energy Input’’ and ‘‘Non-Energy Input’’. Each
‘‘Process’’ produces a ‘‘product’’ to be used by the immediate next
‘‘Process’’ until the final ‘‘product’’ is produced. Based on such approach, Fig. 10 can be transformed into a generic multi-process,
multi-input–output ‘‘LCA Main System’’ (Fig. 11). Using fission
power as an example, ‘‘Energy Input’’ to the mining and milling
‘‘Process’’ could be electricity and/or heat (directly produced from
coal, natural gas, or other primary energy sources); ‘‘Non-Energy Input’’ could be the mining facilities (made of concrete, steel, and
other materials); ‘‘product’’ could be U3O8 for immediate next ‘‘Process’’ of conversion to UF6, and environmental emissions associated
with ‘‘Energy Inputs’’ and ‘‘Fuels’’. Similarly, stages identified by different forms of uranium ‘‘products’’, from mining and milling, to
conversion, enrichment, fuel fabrication, power generation, and
spent-fuel disposal, are defined as ‘‘Processes’’ in the ‘‘LCA Main System’’, denoted as P1 to Pn (Fig. 11). The output from each process is
defined collectively as ‘‘product’’ p1 to pn. The ‘‘Energy Input’’ and
‘‘Non-Energy Input’’ are collective representations, which could be
disaggregated (e.g. into facility construction, operation and maintenance, and decommissioning) according to types and analytical
objectives based on available data.
Under such multi-process and multi-input–output system definitions, we re-write the energy balance equations relevant for subsequent analysis. First of all, the ‘‘Energy Input’’ to each ‘‘Process’’:
Fig. 6. Schematic of a broader fuel fabrication system.
V. Nian et al. / Applied Energy 118 (2014) 68–82
73
Fig. 7. New energy input definition for power generation system.
Fig. 8. Formation of life cycle power generation system via system merging.
Fig. 9. Complete life cycle power generation system input–output definition.
Ein ¼
1
X
Ei
ð5Þ
i¼1
where Ei is the generic representation of each type of ‘‘Energy Input’’
to a ‘‘Process’’. In this equation, the variable ‘‘i’’ is an indicator that
distinguishes the different types of activates/technologies of energy
production. This equation also signifies that there can be a theoretically infinite number of activities consuming different types of
‘‘Energy Input’’ within a ‘‘Process’’.
Second, the ‘‘Non-Energy Input’’ to each ‘‘Process’’:
NEin ¼
1
X
NEi
ð6Þ
i¼1
Fig. 10. Generic life cycle power generation system.
where NEi is the generic representation of each type of ‘‘Non-Energy
Input’’ to a ‘‘Process’’.
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V. Nian et al. / Applied Energy 118 (2014) 68–82
Fig. 11. Generic LCA Main System for power generation via PCA approach.
Finally, the carbon emissions from each ‘‘Process’’:
C¼
1
1
X
X
ce;i Ei þ
cne;i NEi þ C Fuel
i¼1
ð7Þ
i¼1
where
C: carbon (GHG) emissions.
c: carbon (emission) intensity of ‘‘Process’’ input.
ce,i: carbon intensity of ‘‘Energy Input’’.
ce,i: carbon intensity of ‘‘Non-Energy Input’’.
CFuel: carbon emissions from the ‘‘Fuel’’.
It is noteworthy that Eqs. (5)–(7) could also be further generalized to depict the total ‘‘Energy Input’’ and ‘‘Non-Energy Input’’ to
the entire multi-process system, and the life cycle emissions from
the system. In the subsequent representation, variable ‘‘n’’ is used
to denote each ‘‘Process’’ of the system. Similar to the concept of
variable ‘‘i’’, variable ‘‘n’’ is used as an indicator to distinguish the
different ‘‘Processes’’ of an ‘‘LCA Main System’’. Thus, in the most
generalizable representation, variable ‘‘n’’ also has a theoretical
infinite upper limit.
3.2. Leveled system and system boundaries
Relative to the definition of the ‘‘LCA Main System’’, we introduce ‘‘(LCA) Sub-system’’, and the ‘‘Leveled Sub-system’’ definitions
in this section. These two definitions would introduce evolutionary
progress in the understanding and application of system boundaries on power generation system. Without clearly and objectively
applied boundaries, an LCA methodology could be biased to produce preferred or misinterpreted results; and decisions made from
these results may lead to negative environmental impacts [35]. As
such, the discussion on system boundaries forms an essential part
of the proposed methodology.
According to [36], there are four dimensions in defining the
system boundaries for a physical product: (1) boundaries between the technological system and nature, (2) restrictions on
the geographical area time horizon, (3) boundaries between the
production of system goods, and production of capital goods,
and (4) boundaries between the life cycle of the product studied
and related life cycles of other products. The system boundaries
arising from these four dimensions may apply well to a manufacturing product (such as car or mobile phone), but it requires
adjustments and adaptations when applied to a power generation
system.
In this paper, we propose the following three dimensions in
defining the system boundaries for power generation: (1) the
boundaries between the technological system and its surroundings; (2) the boundaries between the ‘‘LCA Main System’’ and the
‘‘LCA Sub-systems’’; and (3) the regional boundaries for determining
the geographical locations of each ‘‘Process’’ of the ‘‘LCA Main System’’, and the (cradle-to-grave) timeframe boundaries of the ‘‘LCA
Main System’’. Compared with the four dimensions defined in
[36], our proposed three dimensions corresponds to their 1st, 3rd
and 4th, and 2nd dimensions respectively.
The proposed 1st dimension, adapted from the cyclic process,
states that for a multi-process technological system, ‘‘Energy Input’’
and ‘‘Non-Energy Input’’ were drawn from their surroundings to
produce desired ‘‘product’’ (electricity in this case) at a given loss
(e.g. waste heat) and GHG emissions rejected back into the surroundings. By applying the energy balance equations, one can compute the total energy consumed by the technological system and
total energy loss to the surroundings. With the inclusion of internal
energy generation, one can further compute some important indicators for a given energy system, such as the net energy gain (NEG)
and system energy efficiency.
The proposed 2nd dimension introduces the definition of the
‘‘LCA Sub-system’’. To each ‘‘Process’’ of the ‘‘LCA Main System’’, there
are ‘‘Energy Input’’ and ‘‘Non-Energy Input’’. These inputs are produced by an ‘‘LCA Main System’’ of their own before they are available as ‘‘inputs’’. These input-producing systems, relative to the
‘‘LCA Main System’’ are defined as the ‘‘LCA Sub-systems’’. Between
the ‘‘LCA Main System’’ and ‘‘LCA Sub-systems’’, there exist system
boundaries. This dimension would serve as the fundamental basis
for our subsequent discussions on the accurate tracking of emission streams.
Following the same concept, the ‘‘Energy Input’’ and ‘‘Non-Energy Input’’ to the ‘‘LCA Sub-system’’ are produced by their respective ‘‘LCA Main System’’. The expansion of such a system structure
leads to the establishment of a ‘‘Leveled System Structure’’
(Fig. 12). Under the ‘‘Leveled System Structure’’, each ‘‘LCA Sub-system’’ is defined as ‘‘Leveled Sub-system’’. For simplicity, we denote
the ‘‘Energy Input’’ as ‘‘E’’, the ‘‘Non-Energy Input’’ as ‘‘NE’’, and each
‘‘LCA Sub-system’’ as ‘‘Sys’’. The dotted vertical lines represent the
stepwise expansion to the nth level.
The proposed 3rd dimension defines the geographical and timeframe boundaries. This dimension is somewhat flexible as it can be
influenced by the agenda of an LCA. In general, there are two degrees of freedom in this structure, vertical and horizontal. The vertical expansion of system processes involves the expansion of
geographical locations, and hence the regional boundaries of the
‘‘LCA Main System’’. The horizontal expansion across the leveled
sub-systems involves the expansion of timeframe and also
geographical boundaries. Vertically, there must be at least one
‘‘Process’’ (power generation) involved in the analysis. Horizontally,
Level 0 refers to the timeframe of ‘‘LCA Main System’’; and Level n is
the nth step expansion for tracing the emission streams for inputs.
A complete vertical expansion is the true ‘‘cradle-to-grave’’ assessment of the ‘‘LCA Main System’’. A complete vertical and horizontal
expansion traces back to the first unit of natural resources harvested for energy production since pre-industrialization for the
‘‘LCA Main System’’.
3.3. The energy-emission analysis
Conventional LCA exercises account for the emission streams
from both energy (‘‘Energy Input’’) and materials (‘‘Non-Energy Input’’). However, there is a lack of transparency and accuracy when
including the emission streams from materials. The lack of transparency and accuracy can lead to unreliable results. To improve
the transparency and accuracy in tracking the streams of emissions
relevant to the ‘‘LCA Main System’’, we introduce the following
equation:
V. Nian et al. / Applied Energy 118 (2014) 68–82
75
Fig. 12. Expanded view of leveled system structure.
C ¼ C Ext þ C Int
ð8Þ
where
C: the emission due to Energy or Non-Energy Input.
CExt: the extrinsic emission.
CInt: the intrinsic emission.
The intrinsic emission refers from the conversion of carbon contents of the ‘‘Input’’ when it is utilized by the ‘‘Process’’ of the ‘‘LCA
Main System’’. The extrinsic emission refers to the emissions from
the ‘‘LCA Sub-system’’ that makes the ‘‘Input’’ available to the ‘‘LCA
Main System’’. The conversion of carbon contents of coal into CO2
during combustion produces intrinsic emission. The production
of coal from its associated ‘‘LCA Sub-system’’ produces the extrinsic
emission (Fig. 13). It is also interpreted as the ‘‘embodied’’ emission. In the ‘‘Leveled’’ system structure, there is a clearly defined
system boundary between the ‘‘LCA Main System’’ and its ‘‘Leveled
Sub-systems’’. In equivalence, the extrinsic emission is the same
as the life cycle emission of the ‘‘Non-Energy Input’’. For a transparent and accurate LCA exercise, the inclusion of emission streams
due to ‘‘Non-Energy Input’’ requires careful analysis.
To quantify the discussions on the boundaries, we employ the
decomposition technique to the Kaya Identity [21]:
GDP
Energy
Emissions ¼ Population Population
GDP
Emission
Energy
ð9Þ
To represent a generic life cycle system, we re-write the Kaya
Identity into one that depicts ‘‘product’’ conversion at each process
with its associated energy intensity and carbon intensity in a multi-process system.
pn
En
Cn
¼ pn en cn
pn1
pn
En
pn
NEn
Cn
¼ pn nen cn
¼ pn1 pn1
pn
NEn
C E;n ¼ pn1 C NE;n
ð10Þ
ð11Þ
where
CE,n: carbon emissions from ‘‘Process’’ (Pn) activities due to
‘‘Energy Input’’.
CNE,n: carbon emissions from ‘‘Process’’ (Pn) activities due to
‘‘Non-Energy Input’’.
Fig. 13. Tracking the sources of emissions (intrinsic vs. extrinsic).
76
V. Nian et al. / Applied Energy 118 (2014) 68–82
En: ‘‘Energy Input’’ to support the activities within ‘‘Process’’,
Pn.
NEn: ‘‘Non-Energy Input’’ to support the ‘‘Process’’ activities.
pn1: ‘‘product’’ from upstream ‘‘Process’’, Pn.
pn/pn1: conversion efficiency of ‘‘Process’’ Pn1 or productivity of pn1.
En/pn: energy intensity of ‘‘product’’, pn.
Cn/En: carbon intensity of ‘‘Energy Input’’.
pn: ‘‘product’’ of ‘‘Process’’, Pn.
en: energy intensity of ‘‘product’’, pn.
nen: intensity of ‘‘Non-Energy Input’’ required to produce pn.
cn: carbon intensity of ‘‘Energy Input’’ or ‘‘Non-Energy Input’’.
In the case of a multi-process-input system, the total emissions
due to ‘‘Energy Input’’ and ‘‘Non-Energy Input’’ are given respectively as follows after applying Eq. (8):
CE ¼
"
#
1
1
1
X
X
X
C E;n ¼
pn ðei ðcExt;i þ cInt;i ÞÞ
n¼1
C NE ¼
n¼1
"
#
1
1
1
X
X
X
C NE;n ¼
pn ðnei ðcExt;i þ cInt;i ÞÞ
n¼1
ð12Þ
i¼1
n¼1
ð13Þ
i¼1
The life cycle emission of the ‘‘LCA Main System’’ (CLC) is the
aggregate of ‘‘Process’’ emissions due to ‘‘Energy Input’’ (CE), ‘‘NonEnergy Input’’ (CNE), and ‘‘Fuel’’ (CFuel).
C LC ¼ C E þ C NE þ C Fuel
ð14Þ
Following the same procedure as the life cycle emission derivation, the total ‘‘Energy Input’’ (Esys) and ‘‘Non-Energy Input’’ (NEsys)
are as follows:
Esys ¼
1
X
n¼1
NEsys ¼
1
X
n¼1
1
X
pn ei
!
i¼1
pn 1
X
nei
ð15Þ
!
ð16Þ
i¼1
where
ei: energy intensity of ‘‘product’’ pn by type.
nei: intensity of ‘‘Non-Energy Input’’ required to ‘‘produce’’ pn by
type.
Quantifying the definition of extrinsic emission, we could
obtain the following equation:
C Ext ¼ C E-sub þ C NE-sub
ð17Þ
where
CE-sub: carbon emissions due to ‘‘Energy Input’’ at the immediate
‘‘LCA Sub-system’’.
CNE-sub: carbon emissions due to ‘‘Non-Energy Input’’ at the
immediate ‘‘LCA Sub-system’’.
For transparent and accurate evaluation of the total ‘‘Energy
Input’’, ‘‘Non-Energy Input’’, and carbon emissions, the leveled
sub-system structure should be expanded on both sides of the
‘‘LCA Main System’’ in a balanced manner. Each crossing of the
leveled system boundary involves the decomposition of CInt and
CExt. The accounting of CExt involves the decomposition into CE-sub
and CNE-sub. Conceptual diagrams depicting the procedures of such
an exercise are represented by Fig. 14 on the ‘‘Energy Input’’ side
and Fig. 15 on the ‘‘Non-Energy Input’’ side up to Level 2 expansion.
Based on the decomposition exercise reported in [37], we could
obtain the following life cycle emission equations (under the
assumption that ‘‘Non-Energy Input’’ does not convert nor
decompose into GHG emissions) at Level 0 (CLC,0) and Level 1
(CLC,1):
C LC;0 ¼
"
1
X
n¼1
pn #
1
X
ðei cInt;i Þ þC Fuel
i¼1
ð18Þ
"
#!
0 X
113
1
1
X
an ðej cInt;j Þ
7
6
B
B
CC
1 6
1 B
7
X
X
B n¼1
CC
j¼1
6
C7
B
þcInt;i C
C LC;1 ¼
6pn C7
Bei B
B
C
6
C
B
E
i
@
AA7
n¼1 4
i¼1 @
5
2
0
0
"
#!113
0 X
1
1
X
a
e
ðc
Þ
7
6
B
n
j
Int;j
CC
B
1 6
1 B
7
X
X
CC
B n¼1
j¼1
6
C7
B
C
B
þ
p
ne
6 n
C7 þC Fuel
B i B
C
7
6
B
NEi
AC
@
n¼1 4
i¼1 @
A5
2
ð19Þ
The procedures for analysis at all other levels are similar, and therefore not explained in this paper. Essentially, there are five important
observations: (1) the intrinsic emissions of ‘‘Energy Input’’ (to the
‘‘LCA Main System’’) have been inherited since the beginning of the
decomposition exercise; (2) at each crossing of the system boundary, extrinsic emissions is decomposed into CE-sub and CNE-sub; (3)
cutting off at any level, intrinsic emissions are retained and extrinsic emissions are eliminated; (4) the summation of CE-sub and CNE-sub
in Eq. (17) of a given ‘‘Energy Input’’ or ‘‘Non-Energy Input’’ to the nth
level is effectively the life cycle emission of this ‘‘Input’’ produced at
Level n + 1 (n P 0); and (5) the life cycle emission calculation appears to be only relevant to the intrinsic emissions of leveled ‘‘Energy Inputs’’ and ‘‘Non-Energy Input’’. Under the assumption of ‘‘0’’
intrinsic emission from the ‘‘Non-Energy Input’’, only intrinsic emissions of ‘‘Energy Inputs’’ are relevant to the life cycle mission
equations.
We believe that Observations (3), (4), and (5) are the root cause
of the unreliability in the current LCA methodologies on power
generation. The inaccurate inclusion of emissions streams from
‘‘Non-Energy Input’’ and/or insufficient granularity on emission
streams accounting for ‘‘Energy Input’’ could be causing the huge
variations among the LCA results on fission power. With our proposed methodology, the streamlined procedures for conducting life
cycle emission analysis minimize the problem of under- or overaccounting of emission sources.
4. Case study – a reference LWR plant for Singapore
To test the assumptions and validate the methodology, we employ a reference LWR plant in this case study. Besides emission calculation, we also include a brief calculation on the net energy gain
and system energy efficiency. The reference plant is assumed to
operate under the conditions specified in Table 1. With these conditions, the plant yields 368 TWh (1.3 EJ) of electric energy (Eout)
over its lifetime.
Based on a typical uranium burn-up value of about
45,000 MWD/t-U, we could obtain the uranium requirement for
each ‘‘Process’’ of the fission power generation system based on
uranium fuel cycle calculation in [37]. The uranium ‘‘product’’
requirement for each ‘‘Process’’ is summarized in Fig. 16.
The primary data for our case study are sourced from [37] given
its high data quality and granularity. The study conducted in [37]
was in reference to a model Japanese LWR plant. Both Singapore
and Japan share similar national circumstances, such as economic
development, technological advancement, and land constraint.
Since Singapore does not have a fission power plant, data provided
in [37] serves as a good reference.
Depending on the choice of enrichment method (gaseous diffusion and centrifuge) and locations, we construct a reference global
uranium supply chain (Fig. 17). Based on such uranium supply
chain and our primary data source, we obtain the life cycle emissions due to both ‘‘Energy Input’’ and ‘‘Non-Energy Input’’ values
by Scenario (Table 2) and the average values by ‘‘Process’’ (Table 3).
V. Nian et al. / Applied Energy 118 (2014) 68–82
Fig. 14. Diagram for decomposing at Level 2 on the ‘‘Energy Input’’ side.
Fig. 15. Diagram for decomposing at Level 2 on the ‘‘Non-Energy Input’’ side.
77
78
V. Nian et al. / Applied Energy 118 (2014) 68–82
Table 1
Operating conditions of the reference LWR plant.
Parameters
Values
Installed capacity
Loading factor
Average thermal efficiency
Lifetime
1000 MW
70%
33%
60 Years
These inputs are related to all the identifiable activities of a given
‘‘Process’’, including facility construction, operation and maintenance, decommissioning, and uranium ‘‘product’’ transportation
from one location to another.
The median value of the life cycle emission factors identified for
this paper is found to be about 22.25 t-CO2/GWh. Our case study
result (25.03 t-CO2/GWh) deviates from the median value by about
12.5% (Fig. 18). To better understand the impact of the embodied
emissions of ‘‘Non-Energy Input’’, we applied the system boundary
definitions at Level 0, which exclude the emission streams from
‘‘Non-Energy Input’’. Under our boundary definitions, our case study
result becomes 22.80 t-CO2/GWh (Table 2, Column 2). At the value
of 22.80 t-CO2/GWh, the error margin reduced from 12.5% to less
than 2.5% (Fig. 19). For better illustration, the average life cycle
emission factors were summarized in Table 4.
In absolute terms, the ‘‘Non-Energy Input’’ contributes to less
than 9% of the total emission factor. The difference before and after
the exclusion of ‘‘Non-Energy Input’’ emissions is trivial given that
an emission factor of 25.03 t-CO2/GWh is considered very small
for a power generation technology. However, it is arguable whether
the more than fourfold difference in error margin from the median
value is acceptable. Therefore, it seems to suggest if the inclusion of
emissions due to ‘‘Non-Energy Input’’ requires further analysis on
the data source. More importantly, it requires more detailed analysis at ‘‘Sub-system’’ level for each ‘‘Non-Energy Input’’.
Fig. 16. Uranium fuel cycle calculation results.
Fig. 17. Global uranium supply chain adapted for Singapore’s reference LWR plant.
V. Nian et al. / Applied Energy 118 (2014) 68–82
79
Table 2
Case study results for the reference LWR plant in Singapore.
‘‘Energy Input’’
t-CO2/GWh
(%)
‘‘Non-Energy Input’’
t-CO2/GWh
(%)
Total
t-CO2/GWh
(%)
Scenario 1
31.11
(94.06%)
1.96
(5.94%)
33.07
(100%)
Scenario 2
18.61
(89.03%)
2.32
(10.97%)
20.93
(100%)
Scenario 3
18.68
(88.60%)
2.4
(11.40%)
21.08
(100%)
Average
22.80
(91.12%)
2.23
(8.88%)
25.03
(100%)
Table 3
Average results by ‘‘Process’’ for the reference LWR plant in Singapore (unit: t-CO2/
GWh).
Process
‘‘Energy Input’’
‘‘Non-Energy Input’’
Total
Mining and milling
Conversion
Enrichment
Re-conversion
Power Generation
SF interim storage
SF disposal
1.12
0.28
4.98
0.76
15.13
0.09
0.43
0.33
0.05
0.32
0.02
1.31
0.07
0.12
1.45
0.34
5.30
0.78
16.44
0.17
0.55
Total
22.80
2.23
25.03
Fig. 18. Benchmarking case study results.
5. Discussions
There are multiple benefits with the developed methodology.
First of all, the generic definitions on system and its associated input–output and boundaries enable the representation beyond electricity generation systems. In this sense, the system definition is
capable of representing complex energy technology systems
including future unknown energy systems (a system drawing from
an unknown energy source). The ‘‘Leveled Sub-system’’ concept allows for balanced expansion for complete and accurate accounting
of all emission streams across timeframe and geographical regions.
Fig. 19. Benchmarking case study results with exclusion of ‘‘Non-Energy Input’’
emissions.
The multiple dimensions governing the system boundary definitions guarantee a transparent and unbiased analysis and benchmarking. Fourth, this methodology could be transformed into a
life cycle cost analysis (LCCA) by associating each input with a
price (cost). Finally, this methodology also allows for the assessment of other impacts brought about by the given life cycle of
the system, such as total natural resource consumptions, and total
production of radioactive substance (in the case of fission energy).
These can be systematically computed and assessed based on itemized ‘‘Non-Energy Input’’ to each ‘‘Process’’.
The drawback of our methodology is data intensive, which is
common among all PCA driven methodologies. As our methodology
is primarily driven by high quality data, insufficient data granularity would very likely result in over- or under-counting in the assessment of energy-emission indicators. It is therefore imperative for
life cycle analysts to pay special attention and effort in harvesting
transparent and meaningful data for assessments. To overcome
the issues with data granularity, it requires the government to institute feasible policy from the top down to ensure transparency and
high quality reporting of process energy consumptions by the
respective industries. At the same time, it requires the electricity
producers and technological system suppliers to actively respond
in reporting from the bottom up. We recognize the difficulty and
sensitivity of disclosing such data, but we believe that collective
reporting and publishing of meaningfully normalized values could
large mitigate the sensitivity issues.
We would also like to highlight another point about our
methodology. Following a balanced systematic LCA on power
generation, the inclusion of emissions streams due to the use of
‘‘Non-Energy Input’’ requires the inclusion of extrinsic emissions at
the ‘‘LCA Sub-system’’ level on the ‘‘Energy Input’’ side. In this case,
it could lead to a continuous inclusion of emission from ‘‘Non-Energy
Input’’, resulting in a potentially infinite expansion of the LCA effort
until it reaches the very first unit of natural resources mined at preindustrialization. It is a laborious and resource consuming procedure. Therefore for an LCA focusing on the ‘‘LCA Main System’’, it is
necessary for the analyst to identify suitable cut-off point by exercising boundary definitions.
80
V. Nian et al. / Applied Energy 118 (2014) 68–82
Table 4
Error analysis of life cycle emission calculation case studies.
Method
Emission factor (t-CO2/GWh)
Median value (t-CO2/GWh)
Error margin (%)
Life cycle emission analysis
Exclusion of embodied emissions of ‘‘Non-Energy Input’’
25.03
22.80
22.25
12.48
2.47
6. Concluding remarks
In this paper, we have developed a generic methodology capable of standardizing all LCA methodologies in the literature for
power generation. Built upon the principle of energy balance, the
concept of ‘‘Kaya Identity’’, and the decomposition technique, we
have clarified the issues with the system boundaries and quantitatively resolved controversies on the inclusion/exclusion of emission sources. With the establishment of the ‘‘Leveled Sub-system’’
concept, the methodology could lead to an accurate accounting
on the streams of carbon emissions.
As validation, a comprehensive numerical case study was conducted on a reference LWR plant. Depending on the choice of uranium enrichment methods, the life cycle carbon emission factors of
fission power range from 21.08 to 33.07 (25.03 in average) t-CO2/
GWh. With the carbon emission streams from ‘‘Non-Energy Input’’
excluded, the carbon emission factor reduced to about 18.68–
31.11 (22.80 in average) t-CO2/GWh. On average, the deviation
from the median reduced from 12.5% to less than 2.5%. On absolute
terms, the ‘‘Non-Energy Input’’ related carbon emissions contributed to less than 9% of life cycle emission factor. On the account
that the life cycle emission factor is already very small, 9% contribution is trivial, especially when considering the long operating life
of the LWR plant. Therefore, it is justifiable for the exclusion of
‘‘Non-Energy Input’’ related carbon emissions for fission power generation technologies to reduce the data intensity of an LCA
exercise.
Lastly, the proposed methodology can be considered as possible
improvements to the existing computerized LCA tools, such as GaBi
and SimaPro. The proposed methodology can also be computerized
as a standalone tool for LCA on energy systems. We recommend
that the computerization of the proposed methodology as future
development work.
Appendix A
See Table A1.
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26.4
3
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66
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92–141
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2–20
1.8
19.7
20.9
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10.5–47
2–59
9
8.9
2.8–24
34.1–37.7
15
3–40
15–30
6–26
22.25
200
1.8
111
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