Meta-Analysis of Life Cycle Energy and Greenhouse Gas Emissions

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Meta-Analysis of Life Cycle Energy and Greenhouse Gas Emissions for Priority Bio-based Chemicals Mahdokht Montazeri, George Gregory Zaimes, Vikas Khanna, and Matthew J. Eckelman ACS Sustainable Chem. Eng., Just Accepted Manuscript • DOI: 10.1021/ acssuschemeng.6b01217 • Publication Date (Web): 06 Sep 2016 Downloaded from http://pubs.acs.org on September 15, 2016

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ACS Sustainable Chemistry & Engineering

Meta-Analysis of Life Cycle Energy and Greenhouse Gas Emissions for Priority Bio-based Chemicals Mahdokht Montazeri1, George G. Zaimes2, Vikas Khanna2, Matthew J. Eckelman1 1

Department of Civil and Environmental Engineering, Northeastern University, 360 Huntington Avenue, Boston, MA, 02115 2

Department of Civil and Environmental Engineering, University of Pittsburgh, 4200 Fifth Avenue, Pittsburgh, PA, 15260

Corresponding author: Mathew J. Eckelman, [email protected], +1 617 373 4256

1 ACS Paragon Plus Environment


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Abstract Research and development for bio-based chemicals production has become a strategic priority in many countries, due to the widespread availability of renewable feedstocks and the potential for reduced life cycle greenhouse gas (GHG) emissions and fossil energy use compared to petrochemicals. These environmental benefits are not assured, however, as a multiplicity of processing features (i.e., biofeedstock, conversion platform, energy/solvent recovery) and life cycle modeling factors (i.e., coproducts, allocation scheme, study scope, location) influence the overall GHG emissions and energy use of a bio-based chemical production scheme. Consequently, there has been high variability in reported environmental impacts of bio-based chemical production across prior life cycle assessment (LCA) studies. This meta-analysis considered 34 priority bio-based chemicals across 86 discrete LCA case studies. Most bio-based chemicals exhibited reduced GHG emissions and net energy use compared to petrochemical counterparts, with exceptions including. p-xylene, acetic acid, and adipic acid. Seven priority bio-based chemicals had no reported results, predominantly lignin-derived. GHG emissions reductions were compared against proposed thresholds from the Roundtable on Sustainable Biomaterials (RSB), the International Sustainability & Carbon Certification (ISCC), and those applied to U.S. biofuels under the Renewable Fuels Standard (RFS2) program. ANCOVA and ANOVA statistical tests were utilized to identify process and life cycle modeling factors that contribute significantly to environmental metrics. Conversion platform was found to be a statistically significant (α=0.1) factor for GHG emissions, with thermochemical routes having the highest results, while LCA coproduct allocation scheme was significant for non-renewable energy use. Recommendations for harmonizing and prioritizing future work are discussed.


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Keywords: bio-based chemicals; renewable chemical standard; carbon certification; emission reduction threshold; conversion; allocation; life cycle assessment; categorical variable



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Introduction Biofuels and bio-based chemicals have received significant interest as a potential low-carbon and environmentally sustainable alternative to conventional fossil-based fuels and petrochemicals. As defined by the US Secretary of Agriculture in the Farm and Rural Investment Act of 2002, biobased products are commercial or industrial products that are composed of biological products, renewable agricultural and forestry materials or intermediate feedstocks, in whole or in significant parts.1 The annual production of bio-based chemicals (excluding fuels) is estimated to be 50 million tons,2 dominated by bio-based polymers (55%), oleochemicals (20%) and fermentation products (18%).3 Commercialization of bio-based chemicals is still nascent, and their penetration rate in the global market will be strongly dependent on development of biorefineries.4 The US Department of Agriculture (USDA) estimates that the global chemicals industry is projected to grow 3-6% annually through 2025, with the bio-based chemicals share of that market rising from 2% in 2006 to 22% or more by 2025.5

In order to prioritize research and development efforts, the US Department of Energy (DOE) published a two-volume report listing target bio-based chemicals.6,7 The first volume of this report investigated bio-based chemical candidates derived from the carbohydrate content of biomass (sugar, cellulose, and starch). Some 300 candidates were evaluated based on potential markets and the technical complexity of the synthesis pathways. The synthesis routes were examined as two-part pathways: transformation of sugars to building blocks; and conversion of building blocks into secondary chemicals or families of derivatives.6 The second volume of the report considered potential candidates derived from the lignin portion of biomass. Three categories of products were studied, including: fuel and syngas; macromolecules and aromatics;


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and miscellaneous monomers. Candidates were chosen based on their technical difficulty of production, market risk, building block utility, and whether a pure material or a mixture would be produced.7

The chemical sector is the largest industrial energy user with ~10% of global primary energy use,8 and ranks third among industrial sectors for direct CO2 emissions, after iron and cement.9 The expectation is that bio-based chemicals require less energy to produce, with fewer associated emissions and a more favorable environmental profile than their petrochemical counterparts. Numerous Life Cycle Assessment (LCA) studies have quantified environmental trade-offs from switching to bio-based production of fuels and chemicals, considering impacts of land use change, fertilizer and pesticide runoff besides fossil energy use and air emissions.10,11,12,13,14 For example, Groot and Boern conducted an LCA of polylactic acid (PLA) production from sugarcane in Thailand, and compared the results with that of fossil-based polymer. The study is a cradle to gate analysis including sugarcane cultivation, sugarcane milling, auxiliary chemicals production, transport, and production of lactide and PLA. On a mass basis, bio-based PLA had lower associated GHG emissions and less material and non-renewable energy use compared to the fossil-based polymers; however, PLA had higher impacts in acidification, photochemical ozone creation, eutrophication and land use categories due to agricultural activities, compared to the fossil-derived polymer. 16

In an effort to reduce US dependence on petroleum-based transportation fuel, heating oil, and jet fuel, the national Renewable Fuel Standard (RFS) program was created under the Energy Policy Act of 2005, which sets explicit sustainability criteria for renewable fuels. In 2007, the Energy



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Independence and Security Act expanded upon this program to establish RFS2 by mandating that 36 billion gallons of renewable fuels be added to the transportation fuel mix by 2022. In addition, RFS2 established relative life cycle GHG emission reduction thresholds for three categories of biofuels—conventional biofuel (primarily corn-based), biomass-based diesel, and cellulosic biofuel―compared to the emissions baseline of the gasoline or diesel they replace. As defined by RFS2, life cycle GHG emission reductions of 20%, 50% and 60%, are required for conventional, biomass-based and cellulosic biofuels, respectively.18

The RFS2 criteria can be useful as benchmarks for bio-based chemicals.

There are also

initiatives that propose sustainability criteria specifically for renewable chemicals, such as the Roundtable on Sustainable Biomaterials (RSB), USDA BioPreferred, International Sustainability & Carbon Certification (ISCC), and Bonsucro. RSB and ISCC are multi-stakeholder coalitions that measure sustainability of different renewable fuels and chemicals and specify GHG emissions reduction thresholds, as one of the primary criteria in their sustainability measures. GHG reduction thresholds for both programs are assigned based on cradle-to-gate system boundary while inclusion of transport and distribution of target chemical is mandated in ISCC scope but not in RSB. Land use change (LUC) and carbon sequestered in growth phase of biomass are also included in the scope of both standards. GHG reduction thresholds are at least 10% and 35% for RSB and ISCC, respectively.19,20 The BioPreferred program developed by USDA is another program that encourages the use of bio-based products, consisting of mandatory purchasing requirements for federal agencies and their contractors and a voluntary labeling initiative for bio-based products. Primary sustainability criteria in this program is at least 25% bio-based content in the composition of the final product.21 The Bonsucro standard, on the


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other hand, is mostly used for chemicals derived from sugarcane and set field-to-gate GHG reduction threshold for sugarcane production and processing (300% increase for p-xylene production from corn (with a mean value of 371% increase in GHG emissions) to a >100% decrease for PHB production from corn (with a mean value of 177% decrease in GHG emissions), when compared to their fossil-based counterparts. For the corn-based chemicals, Figure 1(a), PHA and p-xylene data showed wide 18 ACS Paragon Plus Environment

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ranges of reported values compared to the average, while most of the other chemicals in this category had their results distributed within the 50% of the average values. Based on reported results, succinic acid, ethyl lactate and PHB had the largest potential for GHG emissions reduction (84%, 87% and 177% reductions, respectively) when using corn as feedstock, while pXylene showed significant increase in GHG emissions compared to its petrochemical counterpart. Based on collected data, more than half of the chemicals in this category meet all three GHG reduction thresholds (RFS, RSB, and ISCC). Carbohydrate-derived glucaric and glutamic acids were studied not as target chemicals but as intermediates for the production of adipic acid and N-methylpyrollidone.68-69 Results for these chemicals showed decreases in GHG emissions compared to corresponding petrochemicals, but similar results were not available for production of glucaric and glutamic acids.

Figure 1b presents the results for GHG change of lignin-derived chemicals. The RFS threshold for this group was 50% reduction, since all of the collected cases were sourced from agricultural and forest residues known as non-corn feedstock. Three out of five chemicals with reported results in this category were studied in a single study while phenol and vanillin both had two sets of results. (GHG results for phenol were within 10% of the average value, so the range of reported results was not wide enough for error bars to be visible.) Bio-based adipic acid and phenol had the highest and the lowest potential in GHG emission reduction, 143% and 35%, respectively. No reported values were found for lignin-derived biphenyl, cyclohexane, cresol or vanillic acid. Other chemicals in this category had two data points at most, which make the average results less reliable and emphasize the need for more LCA studies in this category. Vanillin, methanol, styrene, and adipic acid were reported to have more than 50% reduction 40, 42,



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while phenol was shown to have less potential for GHG emission reduction. However, all of

the chemicals in this category meet RSB and ISCC GHG reduction thresholds.

Figure 1c presents the life cycle GHG results for carbohydrate-based chemicals produced from non-corn feedstocks. PHA, in this category, demonstrated highly varied GHG results, which can be interpreted by the features of production pathways. Based on Patel et al., fermentation is the primary conversion method for this chemical, followed by various downstream processing such as solvent extraction, oxidation, homogenization, enzymatic solubilization or solvent extraction and enzymatic solubilization, combined.36 Evaluation of production pathways showed that synthesis of mid-chain length PHA from fermented dextrose using oxidizing agents minimizes GHG emissions.36 This production pathway represents the lower end for reported GHG estimates. The high boundary corresponds to solvent extraction of fermented rapeseed oil. Low PHA level (up to 8% PHA/dry weight) in rapeseed oil along with coproduction of significant amount of residues in solvent extraction process, led to high levels of GHG emissions.36 Reported GHG emissions of PHB, propionic acid, and succinic acid, on the other hand, were distributed within 30% of their average values. Among the chemicals included in this category, sorbitol, arabinitol, and aspartic acid had no LCA results at all, while PEF, PHB, propionic acid, PHA, butadiene, acetic acid, p-xylene and adipic acid showed less than 50% reduction in GHG emissions, on average; However, PEF and PHB met both RSB and ISCC thresholds. Average values for the remaining chemicals showed more than 50% reduction in life cycle GHG emissions.


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As mentioned earlier, several parameters such as choice of feedstock, conversion method, and co-product handling can have significant effects on life cycle emissions and energy use for biobased chemicals. Results for LDPE provide a useful case study to this effect. According to reported results, non-corn LDPE can meet all three GHG reduction thresholds but the estimates vary significantly across studies, and hence highlight the sensitivity of results to the above parameters. Posen et al. in a series of studies24,

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examined variation in results for GHG

emissions due to uncertainties in modeling parameters. The authors showed that ethylene and polyethylene production from cellulosic and advanced feedstocks (sugarcane and switchgrass in particular) can result in lower emissions than their fossil-based counterparts, but these results have high uncertainty mainly due to limited data for commercial-scale production. Corn-based PE on the other hand, shows higher relative GHG emissions and more confident final results because of the data availability in large-scale. For each of the mentioned feedstocks, fertilizer N2O emissions, land use change and co-production of on-site energy from residues, cause significant variations in estimated GHG savings.24, 62

Considering life cycle energy use, comparative results between energy use values (CED / NREU / fossil energy input) demonstrated wide ranges of estimates for both sugar-based and ligninbased chemicals (Figure 2). As mentioned earlier, energy use of both bio-based and fossil-based chemicals were compared based on equivalent indicators. For carbohydrate-based chemicals, PHB from corn and xylitol from non-corn feedstock, had the highest reduction in consumption of non-renewable energy sources (>85%), while styrene with about 100% reduction was the most favorable compound among lignin-based chemicals. Average values of energy use reported for both sugar-based and lignin-based chemicals varied from 97% reduction for PHB to more than



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100% increase for propionic acid, PEF and p-xylene. PDO, acetic acid, p-xylene, PHB and adipic acid had a wide range of results due to different sources and conversion methods. The expectation is that chemicals with less non-renewable energy use demonstrate lower GHG emissions, as well. However, this correlation depends on other factors such as conversion pathway or co-product handling method.

Figure 2. Relative NREU values for (a) chemicals derived from sugar content of corn feedstock, (b) chemicals derived from sugar content of non-corn feedstocks and (c) chemicals derived from lignin content of non-corn feedstocks, compared to their petroleum counterparts. Note: the range shown in each figure represents relative GHG values with negative numbers indicating GHG emissions reductions and positive numbers indicating GHG emissions increases.


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Figure 3. Life cycle energy use (NREU, CED and fossil fuel input) vs. GHG emissions for biobased chemicals

Figure 3 presents the relationship between absolute values of GHG emission and indicators of life cycle energy use. Blue and orange dots represent sugar-based chemicals while green dots show lignin-based compounds. As expected, NREU and fossil fuel input have strong positive correlations with life cycle GHG emissions (with a slightly higher correlation coefficient for fossil fuel input). Statistical results for CED have fewer data points and show a weak linear



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correlation, perhaps as this indicator includes renewable sources as well as non-renewable sources in estimating life cycle energy use. Corresponding linear regression equations are shown in Figure 3, with 95%-confidence intervals for the slope of regression line are demonstrated using the curved bands.

Table 2 provides a summary of ANCOVA and 1-way ANOVA results for model parameters. The results from Table 2 indicate that for response variable GHG emissions only factor ‘Conversion Platform’ is shown to be statistically significant at the 90% confidence level, while for response variable non-renewable energy use factors ‘Conversion Platform’, ‘LCA Coproduct Handling Method’, and ‘Land Use Change’ are significant, i.e., the p-values for these factors are less than the significance level (α=0.10). In total, these results indicate that the choice of ‘Conversion Platform’ has a statistically significant effect on mean life cycle GHG emissions, while the choice of ‘LCA Coproduct Handling Method’ has a statistically significant effect on mean non-renewable energy use. This is important as the choice of LCA scheme for handing coproducts is subjective, and contingent on the judgment of the LCA practitioner, yet can highly influence the results. Additionally, statistically significant differences in the environmental performance between conversion platforms can help guide and prioritize research into specific conversion and upgrading technologies. Accordingly, Tukey tests were performed to determine if pairwise differences between factor level means are statistically significant. For factor ‘Conversion Platform’ and response variable absolute greenhouse gas emissions, Tukey tests reveal that the means for factor levels ‘Biochemical’ as well as ‘Hybrid’ are statistically different from ‘Thermochemical’. Moreover, grouping information using the Tukey method indicate that factor level means for ‘Thermochemical’ platforms are comparatively higher than that of


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‘Biochemical’ or ‘Hybrid’, (6.68 kg CO2e/kg as compared to 2.02 and 0.90 kg, respectively), detailed results are provided in the SI, see Tables S5-S6. For factor ‘LCA Coproduct Handling Method’ and response variable relative non-renewable energy use, Tukey tests reveal that factor level means for ‘Mass’ are statistically different from ‘Hybrid’, detailed results are provided in the SI, see Tables S26-S27. These results reinforce the need for a standardized approach for dealing with coproducts in a life-cycle framework, so as to accurately benchmark the sustainability of bio-based chemicals, and to provide a fair basis of comparison between LCA studies. Detailed 1-way ANOVA results for ‘Conversion Platform’ and ‘LCA Coproduct Handling Method’ is provided in Table 3 and Table 4, respectively.

Table 2. ANCOVA and ANOVA summary results for bio-based chemicals meta-data

Parameter

Covariate or Factor

Factor Levels

Response Variable

P-value

Complexity Complexity Molecular Weight Molecular Weight Feedstock Feedstock Composition Composition Conversion Platform Conversion Platform Geography Geography LCA Coproduct Handling Method LCA Coproduct Handling Method Land Use Change Land Use Change Complexity Complexity Molecular Weight Molecular Weight Feedstock Feedstock Composition

Covariate Covariate Covariate Covariate Factor Factor Factor Factor Factor Factor Factor Factor Factor Factor Factor Factor Covariate Covariate Covariate Covariate Factor Factor Factor

13 13 2 2 5 5 5 5 4 4 3 3 13 13 2

GHG Absolute GHG Relative GHG Absolute GHG Relative GHG Absolute GHG Relative GHG Absolute GHG Relative GHG Absolute GHG Relative GHG Absolute GHG Relative GHG Absolute GHG Relative GHG Absolute GHG Relative NREU Absolute NREU Relative NREU Absolute NREU Relative NREU Absolute NREU Relative NREU Absolute

0.525 0.788 0.106 0.91 0.933 0.184 0.499 0.415 0.087 0.77 0.242 0.954 0.439 0.742 0.511 0.274 0.12 0.874 0.363 0.26 0.214 0.367 0.83


Statistically Significant (α=10%) No No No No No No No No Yes No No No No No No No No No No No No No No


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Composition Conversion Platform Conversion Platform Geography Geography LCA Coproduct Handling Method LCA Coproduct Handling Method Land Use Change Land Use Change

Factor Factor Factor Factor Factor Factor Factor Factor Factor

2 4 4 5 5 4 4 3 3

NREU Relative NREU Absolute NREU Relative NREU Absolute NREU Relative NREU Absolute NREU Relative NREU Absolute NREU Relative

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0.68 0.954 0 0.689 0.809 0.757 0.075 0.585 0.027

No No Yes No No No Yes No Yes

A growing body of scientific work has suggested that GHG emissions resulting from changes in the above and below-ground carbon pools as well as soil organic carbon cycles as a result of direct or indirect transformation of land coverage may negate the carbon neutrality of bio-based products.72-73 As such, ANOVA tests were performed to determine if the inclusion of land-use change impacts had a statistical effect on mean GHG emissions for bio-based chemicals. Twelve studies out of the 86 discrete cases evaluated in this study included LUC impacts, and highlight the large variability in scope and system boundary between cases; however, results from Table 2 indicate that incorporation of LUC impacts did not have a statistically significant effect on mean GHG emissions estimates. It is important to note that the results of this analysis are constrained by a relatively small sample size. As such, additional statistical findings may be gained as more data becomes available in the literature. Detailed ANOVA and ANCOVA results for all parameters are provided in the SI, see Tables S7-S22 and S24-S45.

Table 3. 1-way Analysis of Variance (ANOVA) for factor, ‘Conversion Platform’ for response variable absolute greenhouse gas emissions Source Conversion Platform Error Total

DF 4 79 83

Adj. SS 162.1 1516 1678.1

Adj. MS 40.54 19.19

F-Value 2.11

P-Value 0.087

DF: Degrees of Freedom; Adj. SS: Adjusted Sum of Squares; Adj. MS: Adjusted Mean Squares Response Variable: Greenhouse Gas Emissions (Absolute) Factor: Conversion Platform; Factor Levels: Biochemical, Catalytic, Chemical, Hybrid (i.e., a combination of conversion strategies), and Thermochemical


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Table 4. 1-way Analysis of Variance (ANOVA) for factor, ‘LCA Coproduct Handling Method’ for response variable relative non-renewable energy use Source LCA Coproduct Handling Method Error Total

DF 3 39 42

Adj. SS 3.247 16.959 20.206

Adj. MS 1.0825 0.4348

F-Value 2.49

P-Value 0.075

DF: Degrees of Freedom; Adj. SS: Adjusted Sum of Squares; Adj. MS: Adjusted Mean Squares Response Variable: Non-renewable Energy Use (Relative) Factor: LCA Coproduct Handling Method; Factor Levels: Economic, Mass, System Boundary Expansion, Hybrid (i.e., a combination of two or more)

Two other factors were considered in this meta-analysis. The first is the use of laboratory-scale versus commercial-scale data in the original LCA studies. Scale is an important consideration in LCA modeling, as commercial facilities tend to be better integrated and optimized, for example using solvent recovery processes and on-site energy production in large-scale plants, which tends to result in lower energy use and GHG emissions compared to laboratory results. In this review, only 13 out of 86 collected cases were found to have relied on bench-scale production for their LCI data. A corresponding statistical analysis indicated that “Plant Capacity” is statistically significant for both absolute (p-value = 0.022) or relative GHG emissions (p-value = 0.095) estimates. For absolute GHG emissions, Tukey tests find that factor levels "Pilot Scale" and "Commercial Scale" are statistically different while for relative GHG emissions, Tukey tests do not find any significant differences in factor level means. Tables S47-S52 in the SI show the results of the analysis.

Finally, a sensitivity analysis was performed for the expansion of scope from cradle-to-gate to cradle-to-grave to see if consideration of end-of-life (EOL) shifts the environmental preference or causes bio-based chemicals to miss threshold values for GHG emissions reductions. A single end-of-life scenario was applied so that, for both bio-based and fossil-based chemicals, the



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carbon content of the chemicals is assumed to be released as CO2. For those cases where the biobased chemicals are identical to their fossil-based counterparts (51 cases), these emissions from EOL are the same. For those cases for the bio-based chemicals which were compared with functionally but not chemically equivalent counterparts (30 cases), CO2 emissions from degradation of bio-based chemicals were found to be lower than those of the counterparts in all cases (details in Table S53 of the SI). This will increase the advantage of bio-based chemicals in absolute terms; however, EOL emissions generally make up a larger proportion of cradle-tograve GHG emissions for bio-based chemicals than for fossil-based counterparts, which can reduce the advantage of bio-based chemicals in relative terms. These relative results for cradleto-grave GHG emissions values are reported in Table S54 of the SI.

Ideally, in a more

application-specific context, the length of the use phase and the actual end-of-life disposition of bio-based chemicals would be known so that the benefits of long-term carbon storage could be assessed.

In summary, this review revealed that the majority of LCA studies on bio-based chemicals have focused primarily on sugar-based chemicals, while comparatively little attention has been placed on lignin-derived chemical compounds. Analysis revealed that most, but far from all, bio-based chemicals were able to achieve RSB, ISCC and RFS2-like reductions in GHG emissions relative to baseline petrochemicals. Further, statistical analysis revealed that the choice of conversion platform and LCA coproduct handling method had statistically significant effects on mean GHG emissions and NREU estimates, respectively. Furthermore, the system boundary, scope of the analysis carbon-accounting scheme, and the choice of petrochemical counterpart play an important role in our findings. In order to create a consistent platform for integration of LCA


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cases, the system boundary of this study, was set to be cradle-to-gate excluding GHG emissions and energy use during use phase and end of life of building blocks. Biogenic carbon was considered for the bio-based chemicals while scope and boundaries of the fossil-based counterparts were adapted from the reference literature. However, for specific studies of LCAs of bio-based chemicals with known application, life time and end of life scenario, current results can be further improved by accounting for GHG emissions from landfill or incineration processes, and using more accurate methods for estimation of biogenic carbon such as DayCent and PAS2050.74-75

In light of these findings, several recommendations are provided for future work. First, given the lack of available data, future assessment work should emphasize bio-based chemicals from lignin-based sources. Further, chemicals derived from sugar and lignin content of non-corn feedstock may provide lower GHG emissions related to baseline petrochemicals and merits further investigation. Second, this work shows that the choice of LCA coproduct handling method has a non-trivial impact on non-renewable energy use estimates. As such, a standard allocation method should be agreed upon and applied for bio-based chemicals in order to report and corroborate results between studies. In the context of RFS for biofuels, the recommended LCA method for coproduct handling is avoiding allocation using system expansion.76 However, research has shown that system expansion can produce distorted LCA results for biofuel systems in which coproducts constitute a significant fraction of total economic value, energy flow, or mass flow.60-61, 77 To avoid such pitfalls, it is recommended that LCA practitioners, sustainability scientists, and the chemicals industry collaborate to form a consensus on a standardized LCA approach to account for coproduct flows for bio-based chemicals, perhaps through the creation of



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industry-wide product category rules. Third, estimations of potential GHG reductions are dependent on the choice of conversion platform, thus categorical differences between conversion platforms may be taken into account for a potential Renewable Chemical Standard. Fourth, single metric-based policies fail to capture broader environmental externalities, such as ecological or health-related trade-offs, and may result in unintended environmental consequences. Accordingly, multiple LCA metrics should be concurrently analyzed to ensure that biochemical production does not shift environment impacts across domains or outside of the analysis boundary. For example, a single score LCA study for biofuels production by Daystar et al.63 found that impact categories other than GHG emissions such as ecotoxicity, carcinogenics and non-carcinogenics, largely determined the score values and as a result the environmental preference of target fuels. Finally, while bio-based chemicals have the potential for GHG reductions relative to their petrochemical equivalent, further collaboration between industry leaders, sustainability scientists, and policy makers are needed to assess the technical and commercial feasibility as well as broader environmental consequences of a potential Renewable Chemicals Standard.

Supporting Information The Supporting Information is available free of charge on the ACS Publications website. Descriptions and tables for selected bio-based chemicals and petrochemical counterparts; tables of all statistical results; end-of-life GHG emissions results; and cradle-to-grave GHG emissions reductions for bio-based chemicals.


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Acknowledgement This material is based upon work supported by the National Science Foundation Graduate Research Fellowship to G.G.Z. under Grant No. (DGE-1247842). Any opinion, findings, and conclusions or recommendations expressed in this material are those of the authors(s) and do not necessarily reflect the views of the National Science Foundation. The authors declare no competing financial interest.


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For Table of Contents Use Only Meta-Analysis of Life Cycle Energy and Greenhouse Gas Emissions for Priority Bio-based Chemicals Mahdokht Montazeri, George G. Zaimes, Vikas Khanna, Matthew J. Eckelman

Table of Contents (TOC) Graphic. Priority bio-based chemicals from different feedstock types are evaluated against existing and proposed GHG emissions reductions thresholds. Statistical tests evaluate the influence of categorical variables


Meta-Analysis of Life Cycle Energy and Greenhouse Gas Emissions

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