Application of Hybrid Life Cycle Approaches to Emerging Energy

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Application of Hybrid Life Cycle Approaches to Emerging Energy Technologies
The Case of Wind Power in the UK Thomas O. Wiedmann,*,†,|| Sangwon Suh,‡ Kuishuang Feng,§ Manfred Lenzen,|| Adolf Acquaye,^ Kate Scott,# and John R. Barrettr †

CSIRO Ecosystem Sciences, GPO Box 1700, Canberra, ACT 2601, Australia Bren School of Environmental Science and Management, University of California, Santa Barbara, California, United States § Department of Geography, University of Maryland, College Park, Maryland, United States ISA - Integrated Sustainability Analysis, School of Physics, The University of Sydney, NSW 2006, Australia ^ Stockholm Environment Institute, Grimston House, University of York, York, U.K. # Sustainable Consumption Institute, School of Earth, Atmospheric and Environmental Sciences, University of Manchester, Manchester, U.K. r School of Earth and Environment, University of Leeds, Leeds, U.K.

)

‡

bS Supporting Information ABSTRACT: Future energy technologies will be key for a successful reduction of man-made greenhouse gas emissions. With demand for electricity projected to increase significantly in the future, climate policy goals of limiting the effects of global atmospheric warming can only be achieved if power generation processes are profoundly decarbonized. Energy models, however, have ignored the fact that upstream emissions are associated with any energy technology. In this work we explore methodological options for hybrid life cycle assessment (hybrid LCA) to account for the indirect greenhouse gas (GHG) emissions of energy technologies using wind power generation in the UK as a case study. We develop and compare two different approaches using a multiregion input-output modeling framework
Input-Output-based Hybrid LCA and Integrated Hybrid LCA. The latter utilizes the full-sized Ecoinvent process database. We discuss significance and reliability of the results and suggest ways to improve the accuracy of the calculations. The comparison of hybrid LCA methodologies provides valuable insight into the availability and robustness of approaches for informing energy and environmental policy.

1. INTRODUCTION To avoid some of the most extreme consequences of climate change there is growing scientific consensus that global temperature rise should not exceed two degrees Celsius.1 Over 100 countries have adopted this target as a guiding principle for mitigation.2 For the United Kingdom (UK), this would mean a drastic reduction of territorial emissions of 14% annually, allowing the UK to emit a total of only 2.5 Gigatonnes of carbon dioxide equivalents (Gt CO2e) of greenhouse gas (GHG) emissions between 2023 and 2050. At present, the UK is set to use this amount up by 2014 assuming that emissions align with the carbon budgets outlined in the UK’s Low Carbon Transition Plan.3 Scenarios that demonstrate an 80% reduction in UK GHG emissions by 2050 highlight the growing role of the electricity sector in achieving this target.4 It is estimated that carbon dioxide emissions from power stations accounted for 32% of the UK’s total CO2 emissions in 2007.5 Increasing demand for electricity in the UK (e.g., for transport) means that a virtually complete decarbonization of the electricity sector by 2050 will be required. r 2011 American Chemical Society

However, in all the scenarios attempting to define a low carbon pathway for the UK, the indirect GHG emissions across the whole life cycle of power stations are not taken into account. Energy models have ignored the fact that upstream emissions are associated with any energy technology. The requirement to almost replace the entire energy infrastructure over the next 20 years means it is essential to gain an understanding of the scale of indirect emissions. It is important to know how much of the remaining 2.5 Gt CO2e of GHG emissions that the UK has left to emit past 2022 will be used up by providing the new low-carbon electricity infrastructure. Electricity generation by wind power is currently one of the fastest growing renewable energy technologies worldwide, with a trend toward large-scale production, and has been chosen as the Received: March 3, 2011 Accepted: May 26, 2011 Revised: May 24, 2011 Published: June 08, 2011 5900

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Environmental Science & Technology model technology in this study. The wind power sector is growing rapidly,6 despite high costs for infrastructure7 and a low level of subsidies.8 Compared to conventional technologies such as gas, coal, or nuclear power, the share of electricity produced by wind power is still small in the UK (1.8% in 2008).5 However, the output from wind power has grown between 25% and 50% in recent years. At the end of July 2010, around 1200 Megawatt (MW) of offshore wind power was generating electricity in the UK, making it the leading country in wind power deployment.9 Several new wind parks with a capacity of about 20 Gigawatt (GW) are planned, most of them offshore. And the race is on to build large 10 MW offshore machines. In this paper we explore two hybrid life cycle assessment (LCA) methods to account for the indirect GHG emissions of wind power generation. The goal of the work is to build technology-specific processes into an environmental-economic, multiregion input-output modeling framework that enables the modeler to gain an understanding of the magnitude of economywide GHG emissions of future energy supplies. We introduce both approaches
Input-Output-based Hybrid LCA and Integrated Hybrid LCA
in Section 2. To our knowledge this is the first time that a fully integrated hybrid system in a biregional supply and use framework has been described. As real company or process data were not available for the analysis we reverted to using data from the Ecoinvent database for the process of manufacturing and operating wind turbines. Results, a sensitivity analysis, and a comparison with other studies are presented in Section 3. In the discussion (Section 4) we focus on the limitations and shortcomings of the analysis and make suggestions for improvements.

2. METHODS AND DATA Hybrid LCA is seen as state of the art in life cycle assessments and carbon footprint analyses,10
16 and several real-world applications have been presented recently.17
26 Both methods employed in this work
Input-Output-(IO)-based Hybrid LCA and Integrated Hybrid LCA avoid truncation that can lead to erroneous rankings of LCA results (see e.g. Figure 7 in ref 17). The basic layout of our input-output framework is a tworegion model based on supply and use tables for the UK and the rest of world (ROW).18 Supply and use tables (SUTs) are seen as superior to input-output tables because a) they contain commodity as well as industry detail, and b) they allow working with rectangular supply and use tables which would otherwise have to be aggregated into square input-output tables with an associated loss of detail.19 Furthermore, SUTs provide greater flexibility in allocating inputs and outputs of multiproduct processes (should this become a necessity in future work).20 Full details of the SUT framework are provided in the Supporting Information of this article. For IO-based Hybrid LCA we followed the suggestions made by Joshi21 (in his EIO-LCA Models III to VI) and approximated the actual input requirements of the desired wind power subsector by using information from process analysis. To this end we split the electricity industry and product sectors in eleven subsectors, including the desired subsector of electricity generation by wind power, by using data on total turnover and amount of electricity generated (see the Supporting Information for details). After this pro-rata disaggregation all subsectors still have the same input and sales structure, i.e. the technology coefficients

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in columns and rows of subsectors are identical. As a next step we specified the input bundle of the UK wind power industry, including imports, with “real” data reflecting the “true” requirements. Ideally, these data would be compiled from financial accounts data of all companies forming the sector in the actual year of analysis. We did not have access to such primary data and therefore reverted to the following procedure. Inventories of materials, electricity, and products in physical units for the manufacturing, operating, and decommissioning of wind turbines were derived from the Ecoinvent database (http://www.ecoinvent.org). The unit process requirements (physical inputs) of a 2-MW offshore wind power plant were used as a substitute for real company data, assuming that this type of wind turbine most closely represents the situation in the UK in the near future. The Ecoinvent life cycle inventory (LCI) data include the construction, operation, and decommissioning of the concrete foundation of the wind turbine, the tower, the transformer, the assemblage, the rotor blades, and the mechanical and electronic components within the nacelle. A lifetime of 20 years and a capacity factor of 30% have been assumed in the Ecoinvent data set.22 The scope of our analysis also includes the connection of wind turbines to the grid but not the transmission and distribution of electricity after connection to the grid. We deem transmission and distribution to be part of a separate economic sector. We converted the physical unit process inputs into equivalent expenditure using the unit prices of production for domestic and imported manufactured goods. These expenditure equivalents were then used to replace corresponding original transactions in the wind power sector column of the use table. In order to avoid double counting, those transactions in the original use column were set to zero that correspond with other products included in the process (Ecoinvent) database but are not used by the wind power sector. This also included expenditure on transmission and distribution of electricity as we calculate the life cycle inventory “at plant”. However, inputs from sectors that have no corresponding product in the process database were left unchanged, so as to account for flows that have been excluded from the process analysis. These mainly include services but also products such as office stationary, computers, and furniture, but also more indirectly used products such as food, textiles, footwear, soap, and detergents, etc. As a result, in our SUT framework we obtained an industry column that explicitly and exclusively represents the production requirements of wind power companies as well as a product row that exclusively represents the product of electricity generated by wind power in the UK. In the supply table, we set the principal product to 100%, i.e. we assumed that there is no coproduction. From an environmental analysis point of view such a simplification makes sense, as environmental burdens can be unambiguously linked to the underlying production processes. In the reality of a modern economy, however, electricity from wind power will not only be generated by wind-farmoperating companies but also by other electricity companies as a byproduct. Hence, our (hypothetical) wind power sector represents all wind power operations rather than all wind power companies. For Integrated Hybrid LCA we followed the approach described by various authors.23
27 An m*m matrix Agp describing the inputs of goods to processes in physical units was linked to an n*n compound technology matrix I
A*ss derived from financial transactions between economic sectors (SUT, see the 5901

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Figure 1. Graphical representation of the hybrid requirement matrix used in this work (to improve legibility, sectors were aggregated by a factor of 20 in the picture).

Supporting Information). This was done via an n*m “upstream matrix” Cu and an m*n “downstream matrix” Cd (see eq 1 and Figure 1). In Cu commodity flows in monetary terms from the IO product sectors were added as additional (upstream) inputs to processes, thus complementing the requirements that had been cut off in the process data. One novelty of our approach is that we used the full-sized process matrix from the Ecoinvent LCA database which distinguishes almost 4000 goods and processes. The Supporting Information of this article contains a separate section describing the construction of the upstream requirement matrix Cu step by step. The downstream matrix Cd can be used to represent flows (in physical units) of goods produced by specific processes to the background economy (IO system) (see refs 28 and 29 for a discussion on downstream cut-offs). In this work we assumed that the total annual output of the good “Electricity from wind power, at plant” is used by the sector “Transmission of electricity” and filled the respective cell in Cd (in kWh/£). Accordingly, we set the row of sales coefficients from the UK sector “Electricity by wind power” in the use tables to zero. The total requirement matrix H can be written as 2 3
Agp
Cd ð1Þ H ¼4 5
Cu I
Ass

including the emissions from each process as well as the emissions from upstream production, H was premultiplied with emissions data and postmultiplied with the demand y for the good in question

Inputs to processes and sectors are represented with a negative sign, whereas outputs are positive. Figure 1 shows a graphical representation of the full hybrid matrix. Because the process part distinguishes 3931 processes/goods and the IO part 224 sectors, H has the dimensions (3931 þ 4*224)2 = 48272. To obtain the total amount q of GHG emissions associated with the production of one functional unit of electricity,

3.1. Modeling Results from This Study. Life cycle CO2 emissions of electricity produced by wind power as calculated by the different methods are presented in Table 1. This includes all CO2 emitted during constructing, operating, grid-connecting, and decommissioning of wind power plants, per unit of output of electrical energy over their lifetime. The life cycle inventories (LCIs) do not include the transmission and distribution of

q ¼ ½B B   H  ½y

ð2Þ

B and B* are vectors representing GHG emissions from each process and GHG emission intensity of each economic sector, respectively. We considered the six greenhouse gases included in the United Nations Framework Convention on Climate Change (Kyoto Protocol). y is a vector containing zeros and the functional unit of the process system, representing final demand in physical terms (here: 1 kWh of electricity by wind power). A smaller version of this model setup has also been described by Feng et al.30 Finally, the Path Exchange Method (PXC) as introduced by Lenzen and Crawford31 is based on the technique of structural path analysis (SPA) and was used in this work to explore the possibility of adjusting the life cycle inventory obtained by IO-based Hybrid LCA. Individual paths represent specific supply chain impacts and can be adjusted by the LCA practitioner if or when additional primary data become available.

3. RESULTS

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Table 1. Results for CO2 Life Cycle Emissions from Wind Power by Calculation Method and Main Input Category (UK, 2004) CO2

Process Analysis

Integrated Hybrid LCA

IO-Based Hybrid LCA

cement

2.15

2.46

8.49

g CO2e/kWh

iron

0.34

1.70

1.04

g CO2e/kWh

steel

5.19

16.8

14.4

g CO2e/kWh

metals

0.24

0.32

0.52

g CO2e/kWh

metal forming

0.83

1.80

2.45

g CO2e/kWh

plastics

3.11

3.55

1.52

g CO2e/kWh

electricity

0.38

0.39

0.39

g CO2e/kWh

transport disposal

0.25 0.43

0.33 0.45

0.27 0.01

g CO2e/kWh g CO2e/kWh

other

0.50

0.94

0.64

g CO2e/kWh

total

13.4

28.7

29.7

g CO2e/kWh

Figure 2. Relative contributions of main inputs to the total CO2-LCI for wind power calculated by different methods (“Metals” exclude iron and steel).

electricity. Detailed results for six greenhouse gases have been presented in the Supporting Information. A pure Process LCA of a 2-MW offshore wind power plant using the process matrix and data from the Ecoinvent database yields an LCI for CO2 of 13.4 g per kiloWatthour (g/kWh).22 Adding inputs from IO sectors not covered by the process analysis more than doubles this value to 28.7 g/kWh for Integrated Hybrid LCA and 29.7 g/kWh for IO-based Hybrid LCA. These two latter numbers equate to 56 and 58 kt CO2, respectively, for the total, economy-wide emissions of the UK wind power sector in 2004 (for comparison, if the sector’s total amount of electricity
1939 GWh
had been generated with the average energy mix in the UK, the emissions would have been approximately 948 kt CO2).32 Compared to process analysis the hybrid approaches both yield an about three times higher CO2-LCI for steel. In both hybrid approaches steel is the main contributor, making up about 50
60% of the total CO2-LCI (see Figure 2). Obviously, some higher upstream production processes connected to steel making are not accounted for in the pure process analysis. Nevertheless, various other factors may have contributed to the difference

between the process and the hybrid analysis results. Such factors may include errors in process input estimates, errors in price conversion, errors due to disaggregating the input-output table, and other parametric and systematic errors that may exist in the models used. Cast iron and metal forming also show significantly higher values in the hybrid LCAs. The contribution of cement (concrete) is most prominent in the IO-based hybrid method (making up 29% of its total CO2-LCI). The LCI contribution of plastics (3.1 g/kWh in Process LCA) is calculated slightly higher by Integrated Hybrid LCA (3.6 g/kWh) but significantly lower by IO-based Hybrid LCA (1.5 g/kWh). Less substantial inputs are valued similarly between all approaches (e.g., transport and electricity), whereas disposal activities show significantly lower results in IO-based Hybrid LCA. Although the two hybrid approaches result in a very similar total LCI for CO2, the strong differences in the input categories cement, plastics, and disposal are obvious and require an explanation. For example, what might cause the contribution of cement to vary by a factor of 4? Because of the specific setup in our study, only the IO-based Hybrid LCA method completely represents production and emissions patterns specific to the UK in 2004. The data used in the process analysis, on the other hand, are based on a mix of Danish, Swiss, and European technologies.22 The structural path analysis of the IO-based Hybrid LCA method (Table 5 in the Supporting Information) shows the contribution of UK cement manufacturing as 7.9 g CO2/kWh, which represents almost all of the cement LCI of 8.5 g CO2/kWh in Table 1 (minor contributions come from electricity used by cement manufacturing and from suppliers within the same sector). This hints at a comparatively carbon-intensive cement manufacturing process in the UK which is not represented by the two other methods. In Integrated Hybrid LCA a mix of data is applied
Ecoinvent-specific technologies for the main material inputs and UK-specific technologies for the upstream inputs from IO sectors. For one specific process, namely the usage of electricity by moving parts of the wind power plant, we chose a UK-specific technology in the Ecoinvent database. More precisely, in Process LCA and Integrated Hybrid LCA we replaced “electricity, medium voltage, production UCTE, at grid” with “electricity, medium voltage, production GB, at grid”. In both cases this led to an increase of electricity-related emissions by 15%. 3.2. Sensitivity Analysis. A number of factors contribute to uncertainty in Hybrid LCA modeling, including uncertainties in 5903

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Environmental Science & Technology source data, imputation and balancing, allocation, assuming proportionality and homogeneity, concordance, sectoral aggregation, regional aggregation, temporal discrepancies, representativeness of model data, monetary exchange rates (for multiregion models), and price conversion.33
37 For a brief review see ref 38; for a sensitivity analysis of a wind turbine LCA see ref 39. In the case of our model study we argue that the conversion from physical to monetary units for direct inputs to IO-based Hybrid LCA and upstream inputs in the Cu matrix of Integrated Hybrid LCA is likely to constitute a major reason for uncertainty. In both hybrid models the monetarized inputs constitute the factor that distinguishes the hybrid approaches from pure process analysis and pure input-output analysis, respectively. To demonstrate the effect of price conversion we performed a simple sensitivity analysis by varying prices in both hybrid models by (20%, respectively. Integrated Hybrid LCA showed a smaller sensitivity range for CO2 ((10.8%) than IO-based Hybrid LCA ((19.6%). This result is due to the fact that only upstream inputs are affected by price variation in Integrated Hybrid LCA. Detailed results for all greenhouse gases are listed in the Supporting Information. Such a simple sensitivity analysis can, of course, only insinuate the actual range of error margins. A comprehensive uncertainty analysis based on Monte Carlo simulation and industry data considering both precision and accuracy would be necessary to be ultimately conclusive. 3.3. Comparison with Other Studies. Based on process analysis, the wind turbine manufacturer Vestas reports LCIs for CO2 of about 5 to 8 g/kWh for different sizes of wind turbines.40 Larger wind turbines with a higher power output have lower total emissions per kWh on average.41 Offshore wind turbines are often larger than onshore turbines and achieve higher capacity factors which results in slightly lower relative impacts despite the additional infrastructure. Martínez et al. present an Ecoinvent-based process analysis with some data specific to Spain and calculate an average of 6.6 g CO2e/kWh for a 2-MW power wind turbine (6.2
9.3 g CO2e/kWh).42 The authors adopt different assumptions for recycling; on average 1.7 g CO2e/kWh are prevented by recycling. The new concept of a floating wind turbine was assessed by Weinzettel and colleagues who found a process-based GHG LCI of 11.5 to 12.2 g CO2e/kWh, depending on the assumptions for recycling of materials.43 Tremeac and Meunier report CO2LCIs of 15.8 and 46.4 g CO2/kWh for 4.5 MW and 250 W wind turbines, respectively.44 A review by Lenzen reports GHG LCIs for wind turbines of around 15
25 g CO2e/kWh.45 Varun et al. cite studies with results from 9.7 to 123.7 g/kWh for CO2 only.46 Lenzen and Wachsmann’s calculations on wind turbines in Brazil and Germany yield 2
81 g CO2/kWh, depending on the type of wind turbine, the location of production and operation, and the end of life processes.47 Their result of 45 g CO2/kWh for coastal Germany is probably the most comparable to offshore wind power in the UK. According to a comprehensive multivariate regression analysis by Lenzen and Munksgaard, a 2-MW wind turbine (our example) has, on average, a CO2-LCI of 31 g CO2/kWh (see the Supporting Information for details).41

4. DISCUSSION Hybrid life cycle analyses are generally undertaken with the aim to minimize the limitations posed by pure process or

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input-output analysis. In this work we have employed two different hybrid approaches to account for the life-cycle and economy-wide GHG emissions of wind power generation, taking the UK as an example. The Integrated Hybrid LCA and IO-based Hybrid LCA approach should in principle yield the same result except that the IO-based hybrid approach notes everything in monetary values, while the integrated hybrid method may use physical units. In reality differences occur a) because the country of production for processes and IO sectors for both direct and upstream requirements may differ and b) the IO system uses sector-average requirements rather than process-specific inputs. Both cases apply in our study and we infer that, within existing uncertainties, the differences in LCI results for main components are the consequence of a desired reflection of UK-specific technologies. Our analysis is based on certain assumptions and practical limitations which affect the accuracy of the results: • Due to the nonavailability of real industry data, the results reflect the impacts of a specific technology
2-MW offshore wind park
rather than the exact situation in the UK in 2004. However, we think that the surrogate analysis allows for a sound evaluation of economy-wide emissions of wind power in general and for policy-relevant conclusions. • Strictly speaking, inputs from an IO sector to a process might contain more than just one material input. For example, the machinery sector supplying one particular piece of machinery might also supply training and ongoing support. In these cases, the Cu matrix should contain the value of this additional service, complementing the material input already covered by the process matrix. In our analysis, however, we set IO inputs to zero where there is already a material input in Integrated Hybrid LCA. This was because of the lack of more specific data and to avoid representing input flows twice. Appendix A of ref 23 explains the procedures of subtracting the flows that are represented in the process part from the IO part at the supply and use matrix level, which would be a more accurate way of eliminating “double counting” as it is often referred to. Similarly sophisticated methods have been described by other authors.48
50 • It could be argued that in IO-based Hybrid LCA not the electricity subsector should be adjusted with the material inputs but a “machinery sector” manufacturing and supplying wind turbines to an electricity provider that solely supplies “green power”. However, in this study we wanted to model the impacts of a (hypothetical) industry sector that comprises all activities related to wind power generation, including the manufacturing of turbines. • Our hybrid approaches mix up different temporal boundaries. Data from process-based LCA are based on complete life cycles, spanning several decades. Usually a 20-year lifespan for wind turbines is assumed.22,40,51 By using these data for the adjustment of a sector in an annual IO table, it is implicitly assumed that the proportions of manufacturing, operating, and decommissioning are on average applicable to the sector’s actual activities within one year. In reality, construction and manufacturing will be more prominent in the early days of wind power development when the sector is growing fast, whereas operation and decommissioning will be dominant once no new wind parks are built any longer. Furthermore, our process data are for offshore wind power 5904

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Environmental Science & Technology which still played a minor role in 2004. New technologies are being developed such as floating offshore wind turbines.43 • The input requirements in IO-based Hybrid LCA cover all life-cycle processes, including infrastructure-related activities such as the construction of concrete foundations, the wind turbines themselves, or the installation of transmission cables. However, capital investments have not been included as inputs; they have been left as part of final demand in the SUTs. This was due to practical reasons as a detailed breakdown of investment in the wind power sectors was not available for the analysis. A more detailed analysis of investments would be useful to align the temporal boundaries mentioned above and for scenarios of future developments
areas worth addressing in further research. • Finally, while we have included the connection of wind turbines to the national electricity grid, we have not included the transmission and distribution of electricity after connection to the grid. We see these processes as part of a separate economic sector within the input-output model. According to a recent study by Harrison et al. the life cycle GHG emissions of the transmission network in Great Britain is estimated at around 11 g CO2e/kWh of electricity transmitted.52 Operational emissions account for 96% of this with transmission losses alone totaling 85% and sulfurhexafluoride (SF6) emissions featuring significantly. The CO2 embodied within the raw materials of the network infrastructure represents a modest 3%. Compared to Integrated Hybrid LCA, the IO-based Hybrid LCA approach is easier to implement. It requires less effort to compile the model framework because only input-output matrices are required, no process and upstream (downstream) matrices. This means much less data processing upfront and less complicated updating procedures, making IO-based Hybrid LCA an efficient and less expensive alternative to the integrated approach. Furthermore, national input-output tables automatically represent country-specific products and industries and are generally more up to date than information for specific processes. Integrated Hybrid LCA, on the other hand, has the advantage that no monetarization of physical flows is necessary and that a large number of specific processes to match primary data with is available if—as in our case—detailed LCA databases such as Ecoinvent have been integrated. This is an area where IO-based Hybrid LCA can be improved by employing the path exchange method (PXC) based on structural path analysis. Identifying and replacing specific path information is a helpful technique to make explicit adjustments and increase accuracy. As is demonstrated in the Supporting Information, PXC can for example guide further collection of primary data or identify options for the reduction of emissions. The goal of this work was to introduce technology-specific processes in a generalized environmental-economic, multiregion input-output modeling framework in order to evaluate economywide GHG emissions of energy technologies. IO-based Hybrid LCA proved an efficient and straightforward alternative to fullscale Integrated Hybrid LCA to achieve this goal. However, we share Mattila et al.’s opinion that “a careful interpretation of the results [of input-output-based LCA] is necessary in order to understand the influence of aggregation and allocation”.53 Our comparison of hybrid LCA approaches provides valuable insight into the uncertainty and reliability of the data and methods for informing energy and environmental policy. The results will be

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used to inform UK-wide carbon reduction scenarios that take account of indirect GHG emissions from energy technologies.

’ ASSOCIATED CONTENT

bS

Supporting Information. Additional details on methods, data, results, and structural path analysis. This material is available free of charge via the Internet at http://pubs.acs.org.

’ AUTHOR INFORMATION Corresponding Author

*Phone: þ61 2 6242 1767. E-mail: [email protected].

’ ACKNOWLEDGMENT This research formed part of the program of the UK Energy Research Centre and was supported by the UK Research Councils under Natural Environment Research Council award NE/G007748/1. Special thanks go to Reinout Heijungs from CML Leiden for his advice on Ecoinvent data, the CMLCA tool, and hybrid LCA methods. Two anonymous reviewers provided very helpful comments. At the time of writing the manuscript, Thomas Wiedmann, Kuishuang Feng, and John Barrett were also employed at the Stockholm Environment Institute, University of York, York, UK. Furthermore, Thomas Wiedmann and John Barrett are directors of Centre for Sustainability Accounting Ltd., Innovation Way, York Science Park, York, UK. ’ REFERENCES (1) IPCC Climate Change 2007: Synthesis Report - Contribution of Working Groups I, II and III to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change; Intergovernmental Panel on Climate Change: Geneva, Switzerland, 2007. http://www.ipcc.ch/ publications_and_data/ar4/syr/en/contents.html (accessed January 10, 2011). (2) Meinshausen, M.; Meinshausen, N.; Hare, W.; Raper, S. C. B.; Frieler, K.; Knutti, R.; Frame, D. J.; Allen, M. R. Greenhousegas emission targets for limiting global warming to 2 C. Nature 2009, 458 (7242), 1158
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