NREL is a national laboratory of the U.S. Department of Energy, Office of Energy 

Efficiency & Renewable Energy, operated by the Alliance for Sustainable Energy, LLC.

Contract No. DE-AC36-08GO28308

Harmonization of Energy 

Generation Life Cycle 

Assessments (LCA)

FY2010 LCA Milestone Report

November 2010

Ethan Warner, Garvin Heath, and 

Patrick O’Donoughue

Management Report

NREL/MP-6A20-47492

November 2010
NREL is a national laboratory of the U.S. Department of Energy, Office of Energy 

Efficiency & Renewable Energy, operated by the Alliance for Sustainable Energy, LLC.

National Renewable Energy Laboratory

1617 Cole Boulevard

Golden, Colorado 80401

303-275-3000 • www.nrel.gov

Contract No. DE-AC36-08GO28308

Harmonization of Energy 

Generation Life Cycle 

Assessments (LCA)

FY2010 LCA Milestone Report

November 2010

Ethan Warner, Garvin Heath, and 

Patrick O’Donoughue

Prepared under Task No. SA09.2085

Management Report

NREL/MP-6A20-47492

November 2010
NOTICE

This report was prepared as an account of work sponsored by an agency of the United States government. 

Neither the United States government nor any agency thereof, nor any of their employees, makes any warranty, 

express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of 

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Cover Photos: (left to right) PIX 16416, PIX 17423, PIX 16560, PIX 17613, PIX 17436, PIX 17721

Printed on paper containing at least 50% wastepaper, including 10% post consumer waste.
1

1 Introduction

One barrier to the full support and deployment of alternative energy systems and the 

development of a sustainable energy policy is the lack of robust conclusions about the life cycle 

environmental impacts of energy technologies. A significant number of life cycle assessments 

(LCA) of energy technologies have been published. However, the results reported for certain 

technologies vary greatly, thus clouding the overall synopsis and limiting the utility of LCA to 

inform policy. To date, little attention has been given to analyzing the large body of previously 

published results in a way that can reduce the uncertainty and variability in as-published 

estimates of life cycle environmental impacts and increase their value to decision makers.

Methodological inconsistencies (e.g., inconsistent system boundaries, the use of outdated data, 

variations on similar energy process chains, and even simple differences in reporting of results) 

contribute to the variance in results between studies and hinder meaningful comparison. Many 

methodological inconsistencies between previously published LCAs can be aligned, a process 

termed “harmonization,” thereby increasing their collective value by enabling proper comparison 

and more generalized conclusions. Farrell et al. (2006) demonstrated that, given a set of highquality and fully documented LCAs, one can ex post facto harmonize the methods (e.g., system 

boundaries) of multiple studies with dissimilar as-published results. This process can reduce 

variability and uncertainty, yielding a more robust understanding of the true life cycle burdens. 

Harmonization can also help to identify where gaps in our understanding of the life cycle 

environmental burdens of energy technologies remain and can usefully be filled by new research. 

However, not all sources of inconsistency are amenable to harmonization (e.g., consequential vs. 

attributional LCAs) and this process requires a set of studies providing detailed results and 

considerable effort to accomplish. 

For some combinations of technology and estimated environmental impacts where variability in 

as-published estimates are small, simply conducting a statistical meta-analysis on the aspublished estimates of impacts could yield sound, policy-relevant results. In cases where 

methodological inconsistencies produce results with significant variability, greater efforts to 

reduce inconsistencies and glean robust conclusions could be warranted. Focusing on the latter 

case, this report describes the background of this research issue and provides guidance on how 

best to carry out harmonization of previously published LCA results and provide consistency for 

collaborators on the LCA Harmonization project and those wishing to conduct similar analyses. 

An example is made of an ongoing effort to harmonize estimates of GHG emissions from

previously published LCAs of electricity generation technologies. It is also true that not all 

sources of inconsistency among studies ought to be harmonized, but this report leaves the 

decision to the analyst about which dimensions of incongruity are appropriate to harmonize 

based on the specific context of the research question. 

Background 

There are two broad types of LCAs: consequential and attributional. Consequential LCAs track 

the impact of a particular action (e.g., the production of X (typically large) MWh by a given 

electricity generation technology) across the full range of potential, system-level effects in 

comparison to an alternative, baseline scenario. They include evaluation of both the direct and 

indirect effects this action has in terms of changes in production of the primary product, its 

substitutes and competitors, ad infinitum, within the broader, often global, economic-social 
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system, recognizing that there are environmental consequences (good or bad) to all of these 

changes. Therefore, consequential LCAs require a very broad system boundary, much broader 

than typical attributional LCAs. Consequential LCAs are appropriate for analyzing the impacts 

of policy-level decisions, which typically introduce large-scale and long-term system changes. 

The assessment of feedback across multiple systems tends to lead to studies that are highly 

complex. Partially owing to this complexity, consequential LCAs are not amenable to

comparison with attributional LCAs or even other consequential ones.

In contrast, attributional LCAs analyze the direct effects of a system over a fixed period of time

(Plevin 2009). They seek to attribute all of the environmental impacts of a system, with less 

concern for the realistic implementation or comparison to existing scenarios. System boundaries 

of attributional LCAs are fundamentally different from consequential LCAs. Due to the reduced 

complexity and relative methodological congruity, attributional LCAs lend themselves better to 

comparison. Because most published literature is attributional, leveraging the results of existing 

LCAs for harmonization is limited to a review of attributional LCAs.

However, one subcategory of attributional LCA is also not generally amenable to comparison 

with others using alternative methods: economic input-output-based (EIO) LCAs. EIO-LCAs 

define system boundaries within the domestic economy by the monetary linkages among

industry categories. Databases reporting environmental burdens per monetary unit are matrixed 

to these monetary flows to estimate life cycle environmental burdens. This approach differs 

substantially from the more typical attributional LCA method, called process-based LCA. 

Process-based LCA uses an engineering, bottom-up approach to define mass and energy flows in 

each phase of a technology’s life cycle. The two approaches are not amenable to harmonization 

since their structure is so different. Because process-based LCAs are much more common in the 

literature, in this study, EIO-LCAs will be excluded from the pool eligible for harmonization. 

Studies that combine process and EIO-based approaches (so-called hybrid LCAs) need to be 

evaluated on a case-by-case basis to decide whether the differences in methodological structure 

preclude harmonization.

Within the attributional class of literature, LCA results often vary substantially within a given 

technology category. In some cases, this variation can be attributed to human error through 

flawed calculations, sub-optimal methodological choices, poor data selection, or 

misinterpretation of input data; however, much of the variation is due to “legitimate” differences.

For instance, the research question that frames the study can drastically alter the approach and 

thus the result. As an example, one researcher might be concerned with the impacts of utilizing 

local feedstocks, whereas another researcher conducting a similar study could investigate the 

most economical feedstocks. Legitimate alternative results would follow from these two research 

questions; the latter study could assess imported feedstocks, which are likely to utilize 

fundamentally different transportation and production pathways from that of local feedstocks. 

Similarly, legitimate differences of opinion regarding methodology occur, such as the method 

utilized to allocate impacts across multiple co-products or decisions setting system boundaries.

While this report focuses on describing how to harmonize inconsistencies that are amenable to 

harmonization, it is left to the analyst to decide whether some potentially harmonizable factors 

are actually better left unharmonized.
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These variances in approach hinder comparison across studies for a particular technology as well 

as the larger body of LCA literature. A holistic and consistent treatment of LCA literature 

through harmonization can filter out inferior data and methods, screen out analysis that is 

inapplicable to the research questions or use of the harmonized results, categorize work based on 

the technology/process chain scope, reconcile system boundaries and other methodological 

differences, and transform reported metrics into common units/categories. By overcoming these

barriers through harmonization, LCAs of energy systems can more readily be used in other 

research contexts and better meet the needs of researchers and decision makers. For instance, 

harmonized estimates of life cycle GHG emissions will presumably (for instance, as shown by 

Farrell et al., 2006) display less variability than the as-published estimates from the same set of 

literature. Simple statistical estimates of central tendency and variability will be more robust 

when based on the harmonized estimates and could help inform environmental decision making. 

Also, the set of harmonized estimates could form the basis for building a meta-model that could 

provide a “best” estimate (composed of the best attributes of the constituent studies) of 

environmental burdens. The meta-model could be exercised across legitimate ranges of input 

parameters to assess variability and uncertainty more formally. 

2 “Dimensions of Incommensurability”

A 2009 report by Plevin laid out several key “dimensions of incommensurability” among LCA 

studies. In addition, NREL’s ongoing effort to harmonize estimated GHG emissions from 

electricity generation technologies has identified several other important considerations.

These dimensions are organized into four general types, for a total of ten dimensions: 

• LCA Type/Perspective: Generally not harmonizable 

• Temporal and Spatial Dimensions: Some of these dimensions are harmonizable

• Technology System Dimensions: Some of these dimensions are harmonizable 

• Data/Parameter Dimensions: Most often, these are harmonizable.

Table 1 outlines the ten dimensions. The first column lists the title of each of the ten dimensions 

grouped into their respective types. The second column describes both the dimension and its 

cause. For aid in identification, the third column provides common examples encountered in the 

LCA energy literature. The “harmonization barriers” column conveys the relative ability to 

harmonize the dimension. Green cells indicate readily harmonizable dimensions, red cells are 

unharmonizable, and yellow cells are potentially harmonizable, depending on the circumstance. 

Within the cells, the text describes the reason for this characterization; the yellow cells also 

include a description of the circumstances in which they can be harmonized. This table does not 

address all possible situations in the literature; it is offered as a general guide to aid in the 

development of a plan to address each dimension, as needed, for harmonization.
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Table 1: Description, Examples, Barriers, and Actions (in NREL’s LCA Harmonization Project) Taken on “Dimensions of 

Incommensurability” 

"Dimensions of 

Incommensurability"

Description/Cause Example of the Problem Harmonization Barriers Harmonized for 

Electricity?

Dimension of Analysis Type/Perspective

Consequential and 

Attributional LCAs 

(process-based and 

EIO-based)

Different perspectives. 

Consequential LCAs assess 

larger system changes over 

longer periods of time. 

Process-based attributional 

LCAs assess smaller 

system changes over 

shorter periods of time. 

Economic Input-Output 

(EIO)-based attributional 

LCAs link monetary flows 

among industrial sectors 

with environmental burdens 

per dollar spent in those 

sectors. 

Consequential LCAs will 

include system feedback 

impacts.

Ex. 1: Consequential - Quantification 

of greenhouse gas (GHG) impacts of 

indirect land use changes that occur 

through price changes.

Ex. 2: Attributional - Emissions from a 

coal plant smoke stack.

Ex. 3: EIO-LCA - environmental 

burdens associated with spending 

money in a certain economic sector 

including effects resulting from other 

sectors that are economically tied to 

the one in question.

Generally not harmonizable. 

Attributional and 

consequential analyses ask 

fundamentally different 

research questions, whose 

answers are not 

comparable. EIO-LCAs 

methods and boundaries 

differ so significantly from 

process-based that the 

results are also not generally 

harmonizable. Studies that 

combine process and EIObased approaches could be 

considered for harmonization 

after consideration of the 

extent of their 

methodological differences.

No, papers 

screened out.

Market Mediated 

Impacts

Primarily an issue of 

distributing impacts among 

multiple products. 

Attributional approach: 

“Allocate” the impact on the 

basis of some metric 

inherent to the products 

themselves (Ekvall 2001). 

Consequential approach: 

Expansion of the system 

boundaries to use products 

replaced by co-product to 

determine the distribution 

(Ekvall 2001).

Ex. 1: The use of different co-product 

methods produces greenhouse gas 

emission estimates in the range of 

approximately 6 kg/mmBTU to 33 

kg/mmBTU and 45 g/mmBTU to 65 

g/mmBTU for soybean biodiesel and 

corn grain ethanol, respectively. There 

is also no consistency between the 

magnitude of the impact of a particular 

methodology across fuel pathways

(Wang 2009).

Ex. 2: Distribution of the impacts of 

uranium ore mining among multiple 

mine products (Lenzen 2006). 

Some barriers to 

harmonization. Assuming 

product parameters are 

available, harmonization of 

"allocation" methods is 

possible. Switching from

"allocation" to "system 

expansion" is difficult if not 

impossible because of the 

need for data on other 

product markets that may or 

may not exist. The reverse is 

less problematic if product 

parameters are available.

No, but 

categorized
5

"Dimensions of 

Incommensurability"

Description/Cause Example of the Problem Harmonization Barriers Harmonized for 

Electricity?

Temporal and Spatial Dimensions

Context of the Study The context in which the

technology is located, which 

includes existing economic, 

environmental, and social 

surrounding systems it will 

interact with.

Ex. 1: The type and quantity of organic 

material that creates methane 

emissions associated with hydropower 

dams is dependent on the geographic 

location of the dam (Rashad 2000). 

Ex. 2: Power output of a solar cell can 

be changed from one location to 

another by modifying assumed solar 

radiation.

Some barriers to 

harmonization. The context 

that a technology is placed 

in some cases is inseparable 

from the LCA without 

fundamentally altering the 

original study. In these 

cases, it is more useful to 

categorize these dimensions 

and determine when the 

context has an impact on the 

LCA results. In other cases, 

such as background energy 

mix or solar radiation levels, 

harmonization is possible. 

Partially, some 

categorized

Temporally Dependant 

Impacts

Alternative accounting 

methods of day-to-day 

impacts that vary over time, 

one-time impacts, and 

application (or lack thereof) 

of discount rates to quantify 

future impacts in a single 

number.

Ex. 1: Technology degradation over 

time.

Ex. 2: A nuclear plant built now could

have a much different day–to-day life 

cycle GHG emissions profile 40 years 

out due to changes in ore quality used 

(Storm van Leeuwen 2005).

Few if any barriers to 

harmonization. Because this 

is an LCA output reporting 

issue, normalization is 

possible with complete study 

reporting. Proxy information 

may be necessary otherwise 

to perform back calculations.

Yes

Scope of Impacts Range in impacts assessed 

spatially (local, regional, 

international), temporally 

(immediate or long-term 

impacts), and/or how far 

along chemical reaction 

chains the effects are 

followed.

Ex. 1: The global warming potential 

standard in 2001 for methane over 100 

years compared to the 2007 standard 

for methane over 20 years. 

Ex. 2: NOx's multiple air and water 

chemical pathways.

Some barriers to 

harmonization. 

Harmonizable in many cases 

such as GHGs where 

boundaries of impact 

assessment are consistent 

across studies. Not possible 

when geographic and 

temporal 

boundaries/contexts 

substantially differ. 

Only for GHGs
6

"Dimensions of 

Incommensurability"

Description/Cause Example of the Problem Harmonization Barriers Harmonized for 

Electricity?

Technology System Dimensions

Life Cycle Technology 

System Boundaries

Changes in the scope of the 

system boundary of the 

technology process chain.

For nuclear, boundaries would start 

with direct impacts from a nuclear 

plant, then could included less direct 

impacts of mining of uranium; 

construction of the plant; and waste 

handling, and then more distant 

activities such as mine environmental 

cleanup.

Some barriers to 

harmonization. 

Harmonizable when the 

boundaries are clearly 

established and life cycle 

stages do not overlap. Not 

possible when LCA and/or 

sub-stage system 

boundaries are 

fundamentally different at 

the dataset level.

Partially

Life Cycle Variation 

within System 

Boundaries

Changes in methodological 

accounting or the

technology process chain 

(not affecting the 

technology being studied).

Ex. 1: Corn grain ethanol produced in 

the U.S. Midwest using corn grown in 

the Midwest compared to corn grown 

in Georgia (Groode and Heywood 

2007).

Ex. 2: Use of waste feedstocks that 

would have been land-filled 

necessitating the calculation of an 

avoided emissions credit.

Some barriers to 

harmonization. 

Harmonizable when the 

source of the variation is not 

linked to other factors and 

the variation can be added 

or removed (Ex. 2). Not 

possible when variation is 

implicitly linked to other 

factors such as geography 

(e.g., municipal solid waste 

composition).

Partially

Data Input and Output Dimensions

Functional Units Includes explicit 

fundamental unit 

differences (e.g., g/kWh), 

implicit differences in how it 

is calculated (e.g., HHV: 

higher heating value vs. 

LHV: lower heating value), 

and implicit differences in 

time horizons (e.g., average 

over a lifetime).

Multiple uses of biomass for energy 

lead to a functional unit comparable 

across scenarios of heat, electricity, 

and fuel, such as grams/hectare. A 

grams/hectare functional unit is not 

comparable to other electricity 

technologies.

Few if any barriers to 

harmonization. Because this 

is an issue of LCA output 

reporting, most changes in 

functional units can be 

completed using information 

provided within the LCA or 

common unit conversion 

standards.

Yes
7

"Dimensions of 

Incommensurability"

Description/Cause Example of the Problem Harmonization Barriers Harmonized for 

Electricity?

Case Studies, 

Theoretical Estimates, 

and Projections

Different study perspectives 

that use empirical datasets 

for case studies, empirical 

datasets to make theoretical 

inferences about nonexistent technologies, and 

empirical datasets in 

modeling to make 

predictions.

A projection of the impacts of a wind 

turbine for 2005 in a 1995 study 

compared to an experimentally based 

case study in 2005.

Some barriers to 

harmonization. At the 

dataset level, datasets can 

be removed, replaced, or 

projections undone. 

However, at some point, this 

fundamentally changes the 

study to a completely 

different LCA.

Partially

Dataset/Parameter 

Inputs

Human error in 

data/parameter application 

in an LCA or in its original 

generation leading to 

problems with its 

application.

Continued use of outdated databases 

to carry out current photovoltaic (PV) 

LCAs such as Ecoinvent 

(Fthenakis 2010).

Few if any barriers to 

harmonization. Assuming 

the LCA is not fundamentally 

flawed and reporting is 

complete, the dataset can be 

removed or replaced.

Yes
8

Each individual harmonization project will address these dimensions in varying degrees. Ideally,

harmonizing consequential LCAs would take precedence; however, for the aforementioned 

reasons (part 1) this is not possible. It is also not possible to establish general prioritization rules 

for the remaining dimensions. The same issues that create legitimate variation in the LCA 

literature (alternative research questions and methodology) also make the prioritization of 

dimensions dependent on the context of the harmonization project; different research goals, 

prioritization of these goals, and differences in the literature collected will affect the relative 

importance of the individual dimensions. Each harmonization project needs to establish its own 

protocol to fit the individualized research needs and best allocate resources to produce useful 

results.

Despite context-specific considerations, the primary goal of all harmonization projects should be 

to establish what “legitimate” variation exists in the selected literature; it is not possible, nor is it 

desirable, to eliminate all of the uniqueness within the studies and coalesce the results into one 

value. Secondary goals include determining the uncertainty of the estimates and identifying the 

source of the variation that remains. As an example, consider a harmonization project concerning

photovoltaic (PV) LCAs specific to the United States. It may sometimes be useful to generate a 

single average GHG emission estimate for photovoltaic panel construction and installation 

within the United States. However, homogenizing the studies to produce a national average may 

neglect important and legitimate regional variability associated with solar panel construction. 

Eliminating such resolution limits the applicability of the harmonization results to other research 

projects and policy analysis, all of which will have different regional contexts. It is 

counterproductive to generalize the results of LCAs; harmonization intends to align methodology 

and assumptions while maintaining fidelity to the studies’ unique perspectives, detail, and 

insight. 

The last column in Table 1 shows how NREL’s harmonization of LCAs on electricity generation 

technologies approached each of the 10 “dimensions of incommensurability.” Due to its 

comprehensive scope, the NREL harmonization project addressed all of the dimensions. The 

extent of harmonization of any particular dimension was determined by considering the amount 

of work necessary to harmonize compared to the expected gains in insight from reducing 

variability. 

3 Phase-by-Phase Approach to Carrying out a Harmonization 

Project

Conducting an LCA harmonization project requires a phase-by-phase approach rather than a 

linear step-by-step process; characterizing it as “phase-by-phase” references the need for a 

recursive process, whereas “linear step-by-step” refers to a direct, chronological approach. The 

time and resources necessary to process the large body of literature necessitates an iterative 

process to deal with unexpected issues as they arise, the revising of project plans, and the general 

need to rework some phases over the course of the project as new information becomes 

available. The harmonization project proceeds from phase to phase as the bulk of the work for a 

particular phase is accomplished; however, portions of several phases will be occurring 

simultaneously sometimes across several technologies. In many ways, this phased approach is 

similar to the process established by the International Standards Organization (ISO) for 
9

conducting an LCA. In this process, one starts with a goal/scoping stage, moving to data 

collection/organization, and finishing with the assessment. The ISO similarly suggests that

reinterpretation occur throughout the process (ISO 2006). 

Five phases of a harmonization project: 

• Phase 1: Identify the goal of the harmonization 

• Phase 2: Establish harmonization scope, standards, and screening criteria

• Phase 3: Gather, categorize, filter, and extract the to-be-harmonized results

• Phase 4: Carry out harmonization

• Phase 5: Where applicable, construct a meta-model

Phase 1: Identify the Goal of the Harmonization

In the first phase, the problem or research question guiding the harmonization is outlined along 

with the general goals of the project. This process provides the framework in which methods and 

processes will be developed and used in the following phases. For some harmonization projects,

the problem and the end goal of the research will be complex and specific; in the case of NREL’s 

harmonization of electricity generation LCAs, the problem and goals of the project were more 

general, with a focus on comprehensive analysis of all technologies of modern relevance. 

Phase 2: Establish Appropriate Scope of Dimensions, Standards, and Screening 

Criteria 

To formulate a plan of action for the project, it is necessary to establish the appropriate scope and

context of the dimensions to be harmonized, as well as basic standards for the harmonized 

dimensions. This process should be done iteratively with an initial review of relevant literature as 

the content of the studies to be harmonized will greatly determine the scope of the analysis and 

appropriate screening criteria. This scope-setting process includes selecting dimensions to 

harmonize and deciding the extent of harmonization, followed by the selection of boundary 

conditions for the project as a whole. Such conditions include the geographical and temporal 

boundaries. The standards include dimensional units of the results and conversion factors. For 

example, NREL’s electricity harmonization project set the global warming potentials used to 

calculate the impact of the various GHG species relative to that of CO2 to the IPCC 2007 values 

with a 100-year time horizon.

After selecting the boundary conditions and standards, the second step is to establish screening 

criteria to filter the acquired studies. This requires determining the elements necessary to qualify 

a paper for inclusion in the project, determining the characteristics that will be scrutinized to 

identify the quality of the study, and establishing a minimum research quality standard. Utilizing 

screens will ensure that only relevant and high-quality information is gathered and retained,

thereby reducing project work and strengthening the harmonized results. Because many studies 

will report results under multiple scenarios, the quality and relevance screens should be applied 

at the level of the paper; and once it passes this level, at the level of the scenario as well.

NREL’s electricity generation-focused LCA Harmonization project, consistent with its 

comprehensive scope, set broad eligibility criteria regarding system boundaries. There were no 
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geographical boundaries, though technologies were limited to those of importance in the United 

States. Temporally, technologies needed to be of modern or future relevance. The functional unit 

established was a unit of generation (kWh) or capacity (kW); for GHG emissions, this meant a 

functional unit of g CO2e / kWh or g CO2e / kW. When applicable, the impacts are amortized

over the lifetime of the technology. 

As for screening, technologies not deemed relevant to electricity generation (e.g., biomass for 

fuels) were first screened out. The next screen eliminated articles based on the following criteria:

• Electricity is not produced by the technology analyzed

• Not a full life cycle assessment (i.e., if less than two phases of the life cycle are 

evaluated)

• Conference papers less than or equal to five double-spaced pages in length

• Trade journal articles less than or equal to three pages in length

• PowerPoint presentations, posters, or abstracts

• Published prior to 1980 (because LCA as a field didn’t mature until after this 

approximate date).

A second, more rigorous quality screen evaluated articles based on additional criteria:

• Quality of LCA and GHG accounting methods

• Completeness of reporting regarding the technology investigated, the inputs, and 

the results of the analysis

• Modern or future relevance of the technology and its defining input data (life 

cycle inventory).

Phase 3: Gather, Categorize, Filter, and Extract the To-Be-Harmonized Results

In the third phase, the screens are implemented, categorizing and filtering out papers. It is useful 

to keep a record of the studies considered, due to potential changes in project goals or future 

work of a similar nature. The screening process may be carried out in different ways for different 

technologies and metrics due to technology-specific issues. For instance, the magnitude of a 

particular impact (e.g., SF6 emitted from PV) can vary between technologies, implying that a 

screening criterion based on a study including known important factors might be relevant for one 

technology and not for others. Similarly, the relevance of study age will depend on how 

technology has changed over time. A plethora of factors will influence how categorization and 

filtering is applied to each technology.

Information reported in scenarios from studies passing all screens can be extracted during the 

screening process. The boundaries established in phase 2 control what needs to be gathered from 

each study, although several items should always be collected. These include important details or 

descriptions of the technology or product process chain, such as capacity and other important 

parameters of operation. Temporal and spatial dimensions of the technology should also be 
11

documented in case it is necessary to control for technological change over time. Another 

important aspect is to ensure that results are not duplicative across multiple references, e.g., the 

same author group reporting the same results in a conference proceeding and then a journal 

article or a review article simply reprising results reported in the original source. 

As a general rule, if the project is starting phase 3 while phase 2 is only roughly defined or 

incomplete, it is most efficient to extract more information than is needed to prevent rework,

provide the foundation for unforeseen future analysis, and lead to greater familiarization with the 

literature. 

Phase 4: Carry Out Preliminary Harmonization

After categorizing the gathered literature based on non-harmonizable factors such as subtechnology, feedstock, location, etc., several relatively simple preliminary harmonization steps 

can be implemented to reduce (sometimes significantly) the variability in life cycle impact 

estimates with minimal effort. The first step is to convert metrics of interest to the desired 

functional units. This may require several different conversions, such as to correct the heating 

value (HHV vs. LHV), or extraction of alternative data to derive the necessary results in the 

correct functional units (e.g., converting per kWh to per kW). The next step is to align the 

calculations of the various metrics; for instance, correcting the GWPs to the 100-year, IPCC 

2007 values in the calculations of GHG species impact. Similar conversions may be necessary 

for other metrics.

The remaining preliminary harmonization step is to consistently apply the implicit time 

dependent component of the functional unit, either through its removal or its normalization

across estimates. For example, for electricity technologies, the lifetime, efficiency, and capacity 

(or instead of efficiency and capacity, then the annual energy generation) of the technology are

needed to return construction and decommissioning impacts, reported per kWh over the plant 

life, to their one-time cost. If applicable, discount rates can be applied or removed at this time. 

After the implicit time components are removed, normalized results can be generated by 

amortizing the impacts over the lifetime of the plant using a set of proxy plant operation 

parameters applied to each LCA estimate. If no discount rates are being used, no changes are 

needed in the quantification of the fixed impacts, even if those impacts are expected to vary over

time. If these impacts are going to change over time and a discount rate needs to be added or 

removed, such normalization is not possible unless the plant operation impacts are reported in 

the form of a function or a time series.

For the harmonization of electricity generation technologies, no discounting was used, and 

impacts were spread equally over the lifetime of the technology with reporting of one-time 

upstream and downstream emissions in both a per kWh and per KW capacity (functional unit 

selected). LCA results were converted and reported in terms of LHV, and IPCC 2007, 100-year 

time horizon GWPs were used for GHGs when possible. The presence of other impacts were

noted or recorded as reported

At this point, a decision is necessary on which technologies and/or sub-technologies need to 

undergo a more extensive harmonization. This distinction requires assessing whether the pool of 

qualifying references that report life cycle results for the metric of interest is large enough and 

displays enough as-published variability to justify the effort of continued harmonization. If there 
12

is a reasonable number of references (e.g., 10), but the variability in as-published results is low (a 

subjective assessment), then additional harmonization is not necessarily warranted. If the pool of 

references reporting results is small, then it is likely that additional LCA research is needed 

before a harmonization or meta-analysis can be useful. For technologies that do not require 

additional harmonization, basic statistical metrics (e.g., arithmetic mean and standard deviation) 

can be reported at the technology level, sub-technology level, and/or any other established 

categories. 

Phase 5: Where applicable, construct a meta-model

Analytically, phase 5 is a fairly involved process, because construction of a meta-model requires 

extensive review and research into papers for technologies selected to move on to this phase. 

Guidelines for constructing meta-models have been developed based on work produced in 

previous studies and recommendations provided by Plevin (Farrell 2006, Plevin 2009a, Plevin 

2009b). Constructing a meta-model on all previously published literature passing quality and 

relevance screens is unlikely to be a realistic prospect given the time required to research each 

paper for this phase. A selection or sampling of the quality studies collected that best represents 

the range of estimates for a selected metric should be used to construct the meta-model. Five to 

ten papers is recommended, but this number may depend on other factors such as the level of 

variation that may need to be explained.

To construct the meta-model, first, one must create a modeling framework in which data from 

the studies can be inserted for the particular technology or sub-technology. Once the framework 

is established, settle on a normalized and standardized set of parameters to which the input data 

will be converted. For the purposes of transparency and review of work, pre- and postconversion values should be reported. The model framework can be validated by comparing its 

results to the original reference’s results and to other published research to detect and debug 

problems. 

One important step in further harmonization of the selected studies is to align system boundaries. 

The boundaries of each LCA study for the selected metric should be normalized by first creating 

separate, revised instances of the model for each study. Data from each study that falls outside 

the established life cycle boundaries can be deleted from use in the meta-model. When an 

original study does not estimate the impact of a certain process that’s within the standard system 

boundary, proxy data based on other studies judged to be of high quality should be used to fill in 

missing parameters. The decision of whether a best estimate from a single study, averages of 

multiple high quality studies or another method is used to quantitatively determine the value of 

the missing parameter is to be made by the analyst. Throughout the meta-model development 

process, the data sources and assumptions of the original studies should be reviewed to 

determine age, content, quality, relevance, proper interpretation of the underlying source data, 

quantitative calculation errors, etc. which can inform whether other harmonization steps leading 

to further congruity among the selected studies are warranted. The result of the meta-model 

development process, to this point, is the adjustment of the impact estimates of the original 

studies to ones more methodologically consistent. 

A final, optional step is to create a “best” composite impact estimate based on elements selected 

across multiple studies. Whether the best estimate for a given element is an average across 

multiple studies or is an estimate from a single study is a decision for the analyst after carefully 
13

considering the options. Instead of, or in addition to, the creation of a best, composite impact 

estimate another option is to statistically analyze the adjusted study estimates (e.g., arithmetic 

mean and standard deviation).

4 Concluding Thoughts

The results of harmonization should return more robust understanding of variability, uncertainty 

and central tendency in estimates of life cycle environmental burdens. Already, this process as 

applied to several electricity generation technologies has suggested that our knowledge of the life 

cycle GHG emissions from certain technologies is well researched and robust, where for others, 

there are significant gaps in our understanding, resulting from a lack of study or large variability 

in as-published estimates. In this way, the literature review and harmonization process can aid in 

R&D prioritization. Harmonization can also develop the knowledge necessary to built metamodels which, when fully exercised, can quantify the sensitivity of modeled impacts to changes 

in input parameters. Harmonization also compliments life cycle assessment as normally 

practiced, where a single technology and scenario is investigated thoroughly, by providing a 

broader review across variations in technology, location, operation, etc. 

References

Cherubini, F., Bargigli, S., Ulgiati, S. (2008). Life cycle assessment of urban waste management: 

energy performances and environmental impacts. The case of Rome, Italy. Waste Management

28:2552–64.

Curran, M.A., Mann, M., Norris, G. (2005): The international workshop on electricity data for 

life cycle inventories. J Clean Prod: 13:853–862. http://www.springerlink.com/content/

e523371725208gk3/fulltext.pdf

Ekvall, T., Finnveden, G. (2001). Allocation in ISO 14041—a critical review. J Clean Prod

9:197– 208.

. Accessed December 21, 2009.

Farrell, A. E., Plevin, R. J., Turner, B. T., Jones, A. D., O' Hare, M., Kammen, D. M. (2006). 

Ethanol can contribute to energy and environmental goals. Science 

311:506−508. http://www.sciencemag.org/cgi/content/abstract/311/5760/506. 

Fthenakis, V. (2010). Personal communication to Garvin Heath, National Renewable Energy 

Laboratory. January, 2010.

Accessed 

December 21, 2009.

Groode, T.A., Heywood, J. B. (2007). Ethanol: A Look Ahead. Massachusetts Institute of

Technology Laboratory for Energy and the Environment, Report No. LFEE 2007-02 RP,

Cambridge. http://gisdata.nrri.umn.edu/MNConserve/Groups/phase-ii-cost-benefit-analysis/

resources-for-ag-cba/Ethanol_A%20look%20ahead.pdf. Accessed December 21, 2009.

Hellweg, S., Hofstetter, T., Hungerbuhler, K. (2003) Discounting and the environment—should 

current impacts be weighted differently than impacts harming future generations? Int J LCA 8, 
14

8–18. http://www.springerlink.com/content/p276075477208822/fulltext.pdf. Accessed December 

21, 2009.

International Standardization Organization (ISO). (2006). Environmental Management - Life 

Cycle Assessment - Requirements and Guidelines. ISO 14040:2006(E), Geneva.

Intergovernmental Panel on Climate Change (IPCC). (2001). Climate Change 2001: The 

Scientific Basis. Cambridge University Press: Cambridge, UK. http://www.grida.no/publications/

other/ipcc%5Ftar/?src=/climate/ipcc_tar/wg1/248.htm. Accessed December 21, 2009.

IPCC. (2007). Climate Change 2007: The Physical Science Basis. Cambridge University Press: 

Cambridge, UK. http://ipcc-wg1.ucar.edu/wg1/Report/AR4WG1

_Print_Ch02.pdf. Accessed December 21, 2009.

Lenzen, M., Dey, C., Hardy, C., Bilek, M. (2006). Life-Cycle Energy Balance and Greenhouse 

Gas Emissions of Nuclear Energy in Australia. Centre for Integrated Sustainability Analysis –

University of Sydney, Sydney.

Plevin, R. (2009a). Key Issues in Developing an LCA Meta-model. White Paper for Garvin 

Heath, National Renewable Energy Laboratory. July 13, 2009.

Plevin, R. (2009b). Modeling corn ethanol and climate: A critical comparison of the BESS and 

GREET models. Journal of Industrial Ecology 13(4):495-507.

Rashad, S.M.; Ismail, M.A. (2000). Environmental-impact assessment of hydro-power in Egypt. 

Applied Energy 65:285-302.

Storm van Leuwen, JW., Smith, P. (2005). Nuclear power—the energy balance. http://www

.stormsmith.nl/. Accessed December 21, 2009.

Wang, M., Huo, H., Arora, S. (2009). Methods of Dealing with Co-Products of Biofuels in LifeCycle Analysis and Consequent Results within the U.S. Context. Energy Policy. In Press.