NREL Unveils Energy Project Emissions Calculator

Cradle-to-grave greenhouse gas emissions from solar photovoltaics are about 5 percent of those from coal; wind power and solar energy are about equal in emissions; and nuclear energy is on a par with renewable energy, according to a new study by the National Renewable Energy Laboratory, which claims to use a more precise method for calculating life cycle emissions of such technologies.

In Meta-Analysis of Life Cycle Assessment, published in the Journal of Industrial Ecology, NREL analysts looked at more than 2,000 studies across several energy technologies, and “harmonized” the results by applying quality controls and narrowing the range of estimates for GHG emissions.

NREL says its new approach to assessing GHG emissions from energy technologies is more accurate than previous method that relied heavily on estimates. It hopes the study will give stakeholders a clearer view of the likely environmental impacts of various projects.

According to the harmonized results, the median solar photovoltaic project produced 40 grams of CO2 equivalent for every kWh of energy produced, compared to the 43 grams per kWh estimated by the same results before the quality controls were applied.

Median life-cycle emissions from those coal power projects harmonized stood at 979g of CO2 per kWh, slightly less than the 1,001g of CO2 per kWh from the results pre-harmonized. Emissions from natural gas after harmonization averaged out at 467g of CO2 per kWh.

Renewable energy sources are: solar PV, bio-power projects (40g of CO2 per kWh) and concentrated solar power (27g CO2 per kWh).

Wind power (11g of CO2 per kWh) is the only renewable energy with a lower harmonized median life-cycle emissions total than nuclear power, according to the results.

In April, NREL and the EPA released a tool to determine the feasibility of on-site renewable energy projects on vacant and contaminated land. The alternative energy “decision trees” aim to give landowners, communities and elected officials ways to evaluate sites for solar and wind energy potential from a logistical and economic standpoint, without the need for technical expertise.

With the widespread development of competence in life cycle assessment (LCA) and the availability and increasing sophistication of LCA software, LCA studies are legion. This poses a challenge for decision makers and researchers trying to make sense of multiple and competing studies. One way to enable more productive use of this outpouring of effort is to conduct meta-analyses of the studies. Meta-analysis, a term coined in 1976 by Gene V. Glass, an American statistician (Glass 1976), refers to techniques used to combine the results of multiple studies. It is widely used in biomedical, psychological, and educational research, where it involves sophisticated statistical methods of combining evidence.

Efforts at meta-analysis are just beginning in LCA where qualitative and semiquantitative forms of systematic review are more typical. Systematic quantitative review of LCAs are less common. A study of the net energy and greenhouse gas impacts of ethanol by Farrell and colleagues (2006) in Science reconciled key parameters of several important studies to draw conclusions about the findings in the literature on this contentious topic. This work raised the profile of such reviews in the LCA community.

The National Renewable Energy Laboratory (NREL), one of the U.S. Department of Energy’s national energy laboratories, established a project inspired in part by the work of Farrell and colleagues (2006). The team working on this project sought to systematically review and screen all extant LCAs of electricity generation technologies: wind, concentrating solar power (CSP), crystalline silicon and thin-film photovoltaic, geothermal, ocean energy, hydropower, coal, natural gas, and nuclear. The harmonization of life cycle GHG emissions estimates from six of those technologies are presented in this special issue. An additional NREL study has been submitted to the Journal of Industrial Ecology (JIE) and is under review for possible publication in a subsequent issue. The NREL aptly labeled its work the “LCA Harmonization Project,”: The studies represent very ambitious efforts to adjust parameters in papers identified in its systematic reviews of the LCA literature so that more consistent comparisons could be made across the studies and so that the multiple and varied estimates of published GHG emissions could be clarified. At the present state of the science in meta-analysis of LCAs, the NREL studies do not employ the full gamut of statistical techniques used in fields where the underlying research designs are more homogeneous. In modifying key parameters to improve consistency and in the number of studies reviewed, however, the NREL studies go well beyond much of the extant efforts at review articles on LCA.

This special issue also includes a variety of other systematic reviews, discussions of meta-analysis, and related articles submitted in response to an open call for papers. This special issue is a supplement, that is, an extra issue published in addition to the journal’s normal six per year. This extra effort was motivated by the importance and potential of meta-analysis of LCAs to enlighten and elevate understanding in a core research domain for the JIE. Partial funding for the issue is provided by the U.S. Department of Energy (DOE) through the NREL. All articles were reviewed using the JIE’s standard, single-blind review process. The NREL submissions also underwent internal review prior to being submitted to the JIE. However, that review process was separate; all NREL submissions had to pass the JIE peer review process independent of any of NREL’s internal processes for quality assurance and soundness.

This special issue is important both for the results it presents about a variety of key topics of considerable interest—energy technologies, biomaterials, print imaging, desktop computers—and for what it reveals about the efficacy of efforts to combine and review the results of LCA studies. While the work presented here does not employ the sophisticated statistical techniques used in biomedicine and related fields, it does take a significant step beyond qualitative review. Nonetheless, it is important to remember that harmonization and more elaborate forms of meta-analysis only address part of the challenge in making LCAs useful to decision makers. There remains, for example, the key challenge of solving the “it depends” problem, that is, the fact that the answer to the question “what is the environmentally preferable choice” is almost always “it depends”—on the framing of the question, the boundaries of the system investigated, and the options available.

Even with that caveat, I hope the work presented here will inspire further efforts to make use of and make usable the burgeoning LCA literature and to go further in adapting the techniques for meta-analysis developed elsewhere in the scientific literature. I leave it to the reader to judge the value of these efforts and the prospect for further application of meta-analysis in LCA.

What Can Meta-Analyses Tell Us About the Reliability of Life Cycle Assessment for Decision Support?

The body of life cycle assessment (LCA) literature is vast and has grown over the last decade at a dauntingly rapid rate. Many LCAs have been published on the same or very similar technologies or products, in some cases leading to hundreds of publications. One result is the impression among decision makers that LCAs are inconclusive, owing to perceived and real variability in published estimates of life cycle impacts. Despite the extensive available literature and policy need for more conclusive assessments, only modest attempts have been made to synthesize previous research. A significant challenge to doing so are differences in characteristics of the considered technologies and inconsistencies in methodological choices (e.g., system boundaries, coproduct allocation, and impact assessment methods) among the studies that hamper easy comparisons and related decision support.

An emerging trend is meta-analysis of a set of results from LCAs, which has the potential to clarify the impacts of a particular technology, process, product, or material and produce more robust and policy-relevant results. Meta-analysis in this context is defined here as an analysis of a set of published LCA results to estimate a single or multiple impacts for a single technology or a technology category, either in a statistical sense (e.g., following the practice in the biomedical sciences) or by quantitative adjustment of the underlying studies to make them more methodologically consistent. One example of the latter approach was published in Science by Farrell and colleagues (2006) clarifying the net energy and greenhouse gas (GHG) emissions of ethanol, in which adjustments included the addition of coproduct credit, the addition and subtraction of processes within the system boundary, and a reconciliation of differences in the definition of net energy metrics. Such adjustments therefore provide an even playing field on which all studies can be considered and at the same time specify the conditions of the playing field itself. Understanding the conditions under which a meta-analysis was conducted is important for proper interpretation of both the magnitude and variability in results.

This special supplemental issue of the Journal of Industrial Ecology includes 12 high-quality meta-analyses and critical reviews of LCAs that advance understanding of the life cycle environmental impacts of different technologies, processes, products, and materials. Also published are three contributions on methodology and related discussions of the role of meta-analysis in LCA. The goal of this special supplemental issue is to contribute to the state of the science in LCA beyond the core practice of producing independent studies on specific products or technologies by highlighting the ability of meta-analysis of LCAs to advance understanding in areas of extensive existing literature. The inspiration for the issue came from a series of meta-analyses of life cycle GHG emissions from electricity generation technologies based on research from the LCA Harmonization Project1 of the National Renewable Energy Laboratory (NREL), a laboratory of the U.S. Department of Energy, which also provided financial support for this special supplemental issue. (See the editorial from this special supplemental issue [Lifset 2012], which introduces this supplemental issue and discusses the origins, funding, peer review, and other aspects.)

The first article on reporting considerations for meta-analyses/critical reviews for LCA is from Heath and Mann (2012), who describe the methods used and experience gained in NREL’s LCA Harmonization Project, which produced six of the studies in this special supplemental issue. Their harmonization approach adapts key features of systematic review to identify and screen published LCAs followed by a meta-analytical procedure to adjust published estimates to ones based on a consistent set of methods and assumptions to allow interstudy comparisons and conclusions to be made. In a second study on methods, Zumsteg and colleagues (2012) propose a checklist for a standardized technique to assist in conducting and reporting systematic reviews of LCAs, including meta-analysis, that is based on a framework used in evidence-based medicine. Widespread use of such a checklist would facilitate planning successful reviews, improve the ability to identify systematic reviews in literature searches, ease the ability to update content in future reviews, and allow more transparency of methods to ease peer review and more appropriately generalize findings. Finally, Zamagni and colleagues (2012) propose an approach, inspired by a meta-analysis, for categorizing main methodological topics, reconciling diverging methodological developments, and identifying future research directions in LCA. Their procedure involves the carrying out of a literature review on articles selected according to predefined criteria. The analysis highlights the need for improvement in LCA practicability and model fidelity.

The 12 meta-analyses in this special supplemental issue are listed in Table 1. These studies elucidate the GHG emissions of alternative electricity generation technologies (coal, photovoltaics [PVs; crystalline silicon and thin-film PVs in two articles], concentrating solar power [CSP], wind, and nuclear) and carbon-capture and storage, as well as LCA applications to biobased materials and computers. Each study began with the identification of relevant LCAs and followed with an analysis of a subset selected on the basis of screening criteria described in each manuscript. As shown in Table 1, this subset ranged from 5 to 53 LCAs; however, in most cases the meta-analyses covered a larger number of technology systems, as each individual LCA in the selected set quite often compared multiple systems.

Most of the 12 meta-analyses focus on the life cycle GHG emissions of electricity generation by different sources. Coal-fired and nuclear electricity generation systems are harmonized by Whitaker and colleagues (2012) and Warner and Heath (2012). Harmonization of 53 utility-scale coal-fired electricity generation LCAs by Whitaker and colleagues (2012) finds that approximately 99% of life cycle GHG emissions are directly related to the coal fuel cycle (including combustion) such that a first-order estimate of life cycle GHG emissions could be based on knowledge of the technology type, coal mine emissions, thermal efficiency, and the combustion carbon dioxide emission factor alone without requiring full LCAs. This is in contrast to the findings of Warner and Heath (2012) for light water nuclear power. Significant variability remained after harmonization by system boundary and performance parameters, which could be qualitatively explained by variations in assumed primary source energy mix, uranium ore grade, and the selected LCA method (i.e., process chain vs. economic input-output LCA methods).

Solar electricity generation systems are explored in three articles. First, Burkhardt and colleagues (2012) harmonize ten CSP system LCAs and illustrate the use of a two-level harmonization process for parabolic trough and power tower technologies. Utilizing so-called light harmonization, when the solar fraction and several other performance parameters of both technologies are harmonized, a significant reduction in variability compared to the published estimates of life cycle GHG emissions is revealed. A more intensive level of harmonization was then employed on a smaller pool of studies, which included application of consistent global warming intensities of materials in the life cycle inventory and inclusion of auxiliary natural gas and electricity consumption, revealing an even greater reduction in the estimated variability but an increased central tendency compared to the lightly harmonized results (owing to the inclusion of required auxiliary natural gas and electricity consumption, which are often incorrectly excluded from CSP LCAs). In a second investigation of solar electricity by Kim and colleagues (2012), five studies are used to harmonize amorphous silicon (a-Si), cadmium telluride (CdTe), and copper indium gallium diselenide (CIGS) photovoltaic systems by adjusting efficiency, irradiation, performance ratio, balance of system, and lifetime. Although the adjustment of all of these parameters is found to contribute to a reduced estimate of variability, the importance of irradiation, efficiency, and lifetime are highlighted. Similarly, Hsu and colleagues (2012) harmonize 13 LCAs on crystalline silicon photovoltaic electricity generation by adjusting efficiency, irradiation, the performance ratio, and lifetime and also identify irradiation and lifetime as drivers of variability in the results.

The final three studies on electricity generation investigate wind power systems. Price and Kendall (2012) provide a systematic review of LCAs to investigate life cycle GHG emissions for modern wind turbines in a wide study region (studies are from Australia, Brazil, Canada, Europe, India, New Zealand, Taiwan, and the United States). Adjustments made in turbine size, geographic location, and end-of-life treatment in addition to those intended to produce a consistent system boundary across studies are critiqued. The results of the critique are combined with a requirement that LCAs chosen for review include only those with original LCA data. The 18 LCAs passing the screening criteria are then assessed in a scoring rubric designed to assist in an understanding of consistency in LCA meta-analyses. Next, Padey and colleagues (2012) provide a meta-analysis of life cycle GHG emissions of wind electricity on the basis of 19 LCAs for systems recently manufactured and operated in Europe. These authors use a screening approach somewhat similar to that of Price and colleagues, use adjustment levels representing Europe, and find manufacturing materials, load factor as a function of wind speed, and product lifetime to be most influential. Finally, estimates of life cycle GHG emissions from wind electricity are harmonized by Dolan and Heath (2012) in an analysis of 49 LCAs from a global study region. These authors employ a light harmonization approach with less of a focus on differences in manufacturing and end-of-life management. Adjustment of the capacity factor (i.e., the load factor), operating lifetime, and system boundaries revealed that harmonization by capacity factor resulted in the largest reduction in variability in life cycle GHG emissions. Note also that the wind meta-analyses of, for example, Padey and colleagues and Dolan and Heath must not be compared on the basis of the resulting means, as each harmonize/adjust parameters to a different set of conditions.

Schreiber and colleagues (2012) performed a meta-analysis of 15 LCA studies on electricity generation with three carbon capture and storage technologies (postcombustion, oxyfuel, and precombustion) with a focus on GHG reduction for different regions, fuels, and time horizons. They present a condensed overview of methodological variations, findings, and conclusions gathered from the 15 LCAs. Considering all capture technologies, time horizons, or fuels evaluated, the potential climate benefits of these technologies are counterbalanced by impacts on a range of other environmental categories (e.g., acidification, eutrophication, and photochemical ozone creation). The results are significantly sensitive to three parameter sets: power plant efficiency and energy penalty of the capture process, carbon dioxide capture efficiency and purity, and fuel origin and composition.

For the studies beyond those related to electricity generation, Weiss and colleagues (2012) perform a comprehensive meta-analysis on the environmental benefits and burdens of biobased materials, in which 44 LCAs were reviewed. The authors found that biobased materials save both energy and GHG emissions relative to their fossil counterparts. Conversely, biobased materials may increase eutrophication and stratospheric ozone depletion. Differences in impacts on acidification and photochemical ozone formation are inconclusive. The large uncertainty of individual LCA studies highlights the difficulties in drawing general conclusions about the relative environmental merits between different materials.

Teehan and colleagues (2012) provide a systematic review of LCAs on desktop computers aimed at understanding variability and discrepancies among published studies. Specifically, whereas the majority of studies find that the use phase dominates GHG emissions, three studies disagree with the majority. Given this, Teehan and colleagues select and decompose 13 LCAs to the system component, life cycle phase, and inventory flow levels. Their decomposition to the component level was hampered by a lack of transparency in the published studies that did not allow assessment or adjustment of the underlying parameters. Using published data, they find the manufacturing phase at a smaller but substantial level of contribution to the overall results. Alternatively, Teehan and colleagues found much higher transparency in the use-phase data within the studies reviewed. They reveal that assumptions concerning the hours of daily use directly correlate with the dominance of the use phase, and they question the general applicability of low use estimates (e.g., within the context of plug load measurements). As a result, they identify the use phase as dominant for energy demand and contribution to climate change, with the only exception being regions with low GHG electricity generation.

Finally, Gambeta and colleagues (2012) critically review 12 LCAs on consumer imaging equipment using an International Organization for Standardization (ISO) 14040 framework to identify common practices, limitations, and opportunities for improvement and standardization. Their analysis suggests that comparisons across studies are significantly hampered by variability in methods and reporting. They conclude that standardization of the functional unit and the assumptions that are interwoven with it has a high potential to increase quantitative comparability across studies.

This special supplemental issue makes clear that meta-analysis is very useful in clarifying an understanding of impact magnitude and variability and of the underlying technological parameters that drive the results. Meta-analyses of LCAs are becoming more widely recognized in the field for these virtues through special sessions at conferences, for instance the 2010 International Life Cycle Assessment (InLCA) conference (Heath et al. 2010) and the upcoming 2012 Society of Environmental Toxicology and Chemistry (SETAC) World Congress (Heath and Brandão 2012), the call for papers for this special issue that produced many excellent submissions, including some not yet published, and the few publications that preceded the aforementioned (e.g., the multiregression analysis of Lenzen and Munksgaard 2002). However, the results of LCA studies—and the subsequent decisions they support—are dependent on a wide range of factors that make each LCA study unique. This may limit the use of LCA for decision support, unless LCA studies abide by the same methodological guidelines and principles and are thus consistent and comparable. Data quality is often cited as the major bottleneck of robust LCAs, but other factors play a large role. The variability of LCA results does not depend solely on the variability of the data employed, but rather on a range of factors. Methodological choices related to scope, system boundaries, allocation, choice of impact assessment method, as well as other assumptions, make LCA a tool that often generates uncertain outcomes. All these factors decrease the impact LCA could have in supporting decisions in both public policy and business domains. Therefore, more harmonization needs to take place. More standardization could include the adoption of a clear set of criteria to facilitate analysis of how data quality, scope, assumptions, key findings, and the like affect the results, and for the complete reporting of all key assumptions and methods. Therefore the robustness of LCA studies cannot be assessed without an uncertainty analysis.

On a global level, the ISO 14040–44 series (ISO 2006a, 2006b) attempt to provide some level of standardization and harmonization in both methodological and procedural choices and reporting. Recent developments that complement and go beyond the ISO standards come from the European Commission’s Joint Research Centre, including the International Reference Life Cycle Data System (ILCD) handbook (European Commission 2010) and an LCA directory2 containing several LCA studies and using clear fields that structure and facilitate analysis in terms of suitability for consideration in a policy-support context or for meta-analysis purposes. Additional developments include those under the United Nations Environment Programme (UNEP)-SETAC Life Cycle Initiative.3

Many journals have published numerous LCA studies in recent years. In order to ensure the quality and relevance of LCA studies, not only is peer review an important step, but so is conformity to a common set of rules for performing an LCA. Even though there is still no commonly accepted and applied global standard, not even ISO, the articles in this special supplemental issue show that LCA results are, more often than not, pointing in the same direction. This suggests that LCA is already relevant for supporting decisions, though it could be strengthened through meta-analysis of previous research and methodological guidelines for the conduct of future LCAs.

Background and Reflections on the Life Cycle Assessment Harmonization Project

Despite the ever-growing body of life cycle assessment (LCA) literature on electricity generation technologies, inconsistent methods and assumptions hamper comparison across studies and pooling of published results. Synthesis of the body of previous research is necessary to generate robust results to assess and compare environmental performance of different energy technologies for the benefit of policy makers, managers, investors, and citizens. With funding from the U.S. Department of Energy, the National Renewable Energy Laboratory1 initiated the LCA Harmonization Project2 in an effort to rigorously leverage the numerous individual studies to develop collective insights. The goals of this project were to

understand the range of published results of LCAs of electricity generation technologies,
reduce the variability in published results that stem from inconsistent methods and assumptions, and
clarify the central tendency of published estimates to make the collective results of LCAs available to decision makers in the near term.

The LCA Harmonization Project’s initial focus was evaluating life cycle greenhouse gas (GHG) emissions from electricity generation technologies. Six articles from this first phase of the project are presented in a special supplemental issue of the Journal of Industrial Ecology on Meta-Analysis of LCA: coal (Whitaker et al. 2012), concentrating solar power (Burkhardt et al. 2012), crystalline silicon photovoltaics (PVs) (Hsu et al. 2012), thin-film PVs (Kim et al. 2012), nuclear (Warner and Heath 2012), and wind (Dolan and Heath 2012).

As a relatively young field of study, LCA has much to learn from more mature fields in terms of approaches to leveraging existing knowledge for higher-order insights. Meta-analysis is now a robust field within the biomedical and social sciences, whose methods inspired those for the LCA Harmonization Project. The state of the science calls for meta-analysis to be preceded by a systematic review. Key features of systematic review (Neely et al. 2010) that were adopted by the LCA Harmonization Project included

a comprehensive search of published literature to ensure no bias by, for instance, publication type (journal, report, etc.);
multiple, independent reviews of each candidate reference using predefined screening criteria; and
the formation of a multidisciplinary review team composed of LCA experts, technology experts, and literature search experts that met regularly to ensure consistent application of the screening criteria.

Results of the systematic review portion of the LCA Harmonization Project were published in the Special Report on Renewable Energy Sources and Climate Change Mitigation of the Intergovernmental Panel on Climate Change ( While the screens applied to each technology differed (and are described in detail in each of the above-referenced articles), they each consisted of three general requirements:

employed quality and broadly accepted LCA and GHG accounting methods;
reported inputs, scenario/technology characteristics, important assumptions, and results in enough detail to trace and trust the results; and
evaluated a technology of modern or near-future relevance.

To interpret the results of multiple LCAs of a single technology, a deep understanding must be developed of their methods and assumptions. For electricity generation technologies, key factors include system boundary, assumed lifetime of the technology, impact assessment method (e.g., global warming potentials [GWPs] of assessed GHGs), technological performance parameters such as thermal efficiency and capacity factor, and primary energy resource characteristics such as solar resource and fuel heating value. LCAs differ in these attributes often for legitimate reasons, but their inconsistency hampers direct comparison of the results. Therefore the project developed a meta-analytical procedure called “harmonization” that adjusted the previously published estimates to ones based on a more consistent set of methods and assumptions in two main stages.4 First, system harmonization ensured studies used a consistent set of included processes (e.g., system boundary, set of evaluated GHGs) and metrics (e.g., GWPs). Then technical harmonization set certain key performance parameters or primary energy resource characteristics to consistent values chosen to reflect a modern reference system (typically a modern facility operating in the United States).

By reducing variability owing to inconsistent methods and assumptions, the resulting harmonized estimates clarify a technology’s central tendency of life cycle GHG emissions in ways useful for certain analytical applications and policy and investment decisions. Nevertheless, the parameter values chosen through harmonization may not reflect those desired by all users. Therefore, in each article, methods were provided for adjusting harmonized results to alternative conditions. Finally, in all articles, the results for each step of harmonization are reported both independently and cumulatively to maximize transparency, enabling users to select which results—for example, based on system rather than technological harmonization steps—are most applicable to their needs.

Broadly, system harmonization was accomplished by adding or subtracting an element to achieve a common system boundary. Technical harmonization was accomplished by proportional adjustment of the life cycle GHG emissions estimate to the selected value of the performance parameter or primary energy resource characteristic. Each article describes the details of the calculations for each step of harmonization.

All but one article performed “light harmonization,” whereby a larger set of estimates were harmonized at a higher level (e.g., consistent system boundary at the level of life cycle stage), as compared to “full harmonization” (see Burkhardt et al. 2012), which employed a more resource-intensive degree of harmonization on fewer estimates. The full harmonization by Burkhardt and colleagues (2012) included applying consistent global warming intensities of materials (mass GHG emitted per unit mass of a material) within the life cycle inventory and developed a consistent system boundary within life cycle stages.
Strengths and Limitations of Harmonization

One goal of harmonization is to make the estimates of previously published LCAs more consistent, and therefore comparable. Compared to published results, harmonization has been shown to significantly reduce variability in calculated outcomes (i.e., range, interquartile range). In most cases, the median of published estimates is consistent with that of the harmonized results. When analyses or policies require a one- or two-parameter description of life cycle GHG emissions for generic classes of electricity generation technologies (e.g., point estimate and a measure of variability around that estimate), the results achieved through harmonization provide a more precise initial estimate. Refinements can be achieved through application of the customization methods presented in each harmonization article.

By its retrospective nature, any meta-analysis is limited in many ways by the studies selected for analysis. While harmonization attempts to update (e.g., by modernizing assumed thermal efficiencies) and reconcile the scope of previously deficient works (e.g., by adding omitted life cycle stages), it cannot make up for a lack of study of certain technologies or issues. When a technology has been studied less frequently, the conclusions that can be drawn from meta-analysis are likewise limited. Similarly, when design variations within a class of technology have not been studied (e.g., different burners or pollution control technologies for coal, or novel PV manufacturing methods), the distributional characteristics of life cycle GHG emissions (e.g., minimum, maximum, median) may not reflect the true distribution for that technology. For many electricity generation technologies, however, these limitations are countered by their repeated study. Additionally, the inclusion of methods for evaluating different assumptions enables researchers to explore cases not specifically examined in previous literature, thereby helping to mitigate the impact of those literature limitations.

It is important to note that reporting distributional characteristics does not imply that harmonization is an assessment of likelihood for life cycle GHG emissions or a predictive tool. In addition, while the effectiveness of a given harmonization step at reducing variability is an indicator of the degree of influence on life cycle GHG emissions, harmonization does not include a formal sensitivity analysis. Finally, the success of harmonization at improving the precision of the set of previously published estimates of life cycle GHG emissions does not imply that the results are more accurate. In areas where new science, methods, or context have emerged, all previously published LCAs will lack this aspect, and harmonization may not be able to consider its impact. An important example of this issue, which is relevant for many electricity generation technologies with articles in this special supplemental issue, is that nearly all previously published LCAs are attributional rather than consequential in nature. Market-mediated effects of the deployment of generation technologies, for instance, how variable-output renewables require addition of some amount of dispatchable reserve capacity to maintain system reliability, are not typically quantified. In some ways this is a system boundary issue, but to retrospectively incorporate consequential impacts into attributional LCAs is not a simple matter of addition given the often complex and context-specific interactions among technologies and markets. Thus the answer could change depending on how the question is asked. For many decisions, knowing the more precise estimate of life cycle GHG emissions achieved through harmonization, albeit considering the technology in isolation, is a useful starting point.

It is additionally important to understand that the studies passing the screens used in this project do not represent a statistically independent sample. Clustering of published results owing to the use of similar methods could exist along at least one of three dimensions: multiple estimates reported in the same reference, multiple estimates from the same or similar author groups publishing serially, and multiple references citing the same sources of input data. Clustering, if significant enough, could influence the estimates of central tendency, but estimating the degree of bias introduced and adjusting statistics for this bias is challenging.

The LCA Harmonization Project’s collection and screening of English-language LCAs on electricity generation technologies provides a foundation from which much additional research could be more efficiently conducted. Additional generation technologies and impact categories besides those analyzed in the six studies included in this special supplemental issue could be assessed. Meta-models could be developed and validated in areas of substantial previous research, and relationships between studied attributes explored. Results of harmonization could be aligned with the performance of specific technologies or mixes of facilities (e.g. a grid mix) to estimate life cycle GHG emissions of real systems. Harmonization of global warming intensities of materials, as in the work by Burkhardt and colleagues (2012), could be extended to other noncombustion generation technologies. Perhaps more importantly, issues not previously studied, or not yet studied fully, that have been identified in each of the six harmonization articles contained in the special supplemental issue should be addressed, for instance, consequential effects of variable renewables on the electrical grid; surface mining impacts on release of soil carbon to the atmosphere; truncation error in process-based LCAs; and the impacts of changes going forward in several aspects of materials, such as utilization efficiency in manufacturing, availability of ores, and substitutability of alternatives.

Harmonization is a meta-analytical approach that addresses inconsistency in methods and assumptions of previously published life cycle impact estimates. It has been applied in a rigorous manner to estimates of life cycle GHG emissions from many categories of electricity generation technologies in articles that appear in this special supplemental supplemental issue, reducing the variability and clarifying the central tendency of those estimates in ways useful for decision makers and analysts. Each article took a slightly different approach, demonstrating the flexibility of the harmonization approach. Each article also discusses limitations of the current research, and the state of knowledge and of harmonization, pointing toward a path of extending and improving the meta-analysis of LCAs.

The LCA Harmonization Project has been generously funded by the Office of Energy Efficiency and Renewable Energy of the U.S. Department of Energy under contract no. DE-AC36-08-GO28308. All coauthors of the six studies included in this special issue are thanked for their contributions, as well as the peer reviewers of their articles and audience members at various presentations of the results of this project.

1 The National Renewable Energy Laboratory is the principal research laboratory for the U.S. Department of Energy’s (DOE) Office of Energy Efficiency and Renewable Energy (EERE). The laboratory is managed for EERE by the Alliance for Sustainable Energy, LLC (

2 Additional data and results of the project are available at

3 See, for instance, Figure 8 of the Summary for Policy Makers and section A.II.5.2 of the Methodology Annex.

4 These stages are most clearly defined by Whitaker and colleagues (2012), although they are inherent in each harmonization article.


Burkhardt, J., G. Heath, and E. Cohen. 2012. Life cycle greenhouse gas emissions of trough and tower concentrating solar power electricity generation: Systematic review and harmonization. Journal of Industrial Ecology. DOI: 10.1111/j.1530-9290.2012.00474.x.
Dolan, S. and G. Heath. 2012. Life cycle greenhouse gas emissions of utility-scale wind power: Systematic review and harmonization. Journal of Industrial Ecology. DOI: 10.1111/j.1530-9290.2012.00464.x.
Hsu, D., P. O’Donoughue, V. Fthenakis, G. Heath, H-C. Kim, P. Sawyer, J-K. Choi, and D. Turney. 2012. Life cycle greenhouse gas emissions of crystalline silicon photovoltaic electricity generation: Systematic review and harmonization. Journal of Industrial Ecology. DOI: 10.1111/j.1530-9290.2011.00439.x.
Kim, H.-C., V. Fthenakis, J.-K. Choi, and D. Turney 2012. Life cycle greenhouse gas emissions of thin-film photovoltaic electricity generation: Systematic review and harmonization. Journal of Industrial Ecology. DOI: 10.1111/j.1530-9290.2011.00423.x.
Neely, J. G., A. E. Magit, J. T. Rich, C. C. J. Voelker, E. W. Wang, R. C. Paniello, B. Nussenbaum, and J. P. Bradley. 2010. A practical guide to understanding systematic reviews and meta-analyses. Otolaryngology—Head and Neck Surgery 142: 6–14.
Web of Science® Times Cited: 3
Warner, E. and G. Heath. 2012. Life cycle greenhouse gas emissions of nuclear electricity generation: Systematic review and harmonization. Journal of Industrial Ecology. DOI: 10.1111/j.1530-9290.2012.00472.x.
Whitaker, M., G. Heath, P. O’Donoughue, and M. Vorum. 2012. Life cycle greenhouse gas emissions of coal-fired electricity generation: Systematic review and harmonization. Journal of Industrial Ecology. DOI: 10.1111/j.1530-9290.2012.00465.x..