Life Cycle Assessments on Battery Electric Vehicles and Electrolytic Hydrogen: The Need for Calculation Rules and Better Databases on Electricity
Abstract
:1. Introduction
- What is the uncertainty in applying LCI data from existing leading databases?
- What is the effect of the fact that the LCI data are, in practice, 4–8 years old on average?
- How to deal with the fluctuations over time of the carbon intensity of electricity and time-related allocation on a time scale of 1 h?
- When does it make sense to apply a Guarantee of Origin or the residual mix, and when is it better to apply the country consumption mix?
- How to deal with nuclear power in relation to greenhouse gas emissions (the issue of comparing ‘apples with oranges’ by the selection of midpoints and endpoints in LCA)?
2. Methods: Specifications and Regulations
” …should address the following: (a) time-related coverage: the age of data …; (b) geographical coverage: geographical area from which data for unit processes should be collected to satisfy the goal of the study;…” (§4.2.3.6.2).
“LCI data should represent the smallest, appropriate geographical unit, depending on the goal of the LCI/LCA study and the intended applications. If e.g., the use of an energy-using consumer product in France would be the scope of the data set, the corresponding electricity market consumption mix …. were to be considered, i.e., not European or Global average conditions”.
“… for electricity and heat delivered via a larger energy transmission system, secondary data that is as specific to the product system as possible (e.g., average electricity supply emission factor for the country in which the electricity is used)”.
“Note 3. In countries where the flow of renewable electricity is accurately accounted for, the requirement 7.9.4.2 allows companies using renewable electricity, or purchasing renewable electricity through a dedicated tariff, to use the GHG emission of the renewable electricity (rather than grid-average carbon intensity) when calculating the emissions arising from their processes”.
“GHG emissions offset mechanisms, including but not limited to voluntary offset schemes or nationally or internationally recognized offset mechanisms, shall not be used at any point in the assessment of the GHG emissions of the product. Note… The use of an energy source that results in lower GHG emissions to the atmosphere and therefore achieves a lower emission factor, such as renewable electricity (see 7.9.4) or …, is not a form of offsetting”.
“As with data from other emission sources, companies should select electricity emission factors that are geographically specific to the electricity sources used in the product inventory. When an electricity supplier can deliver a supplier-specific emission factor and these emissions are excluded from the regional emission factor, the supplier’s electricity data should be used. Otherwise, companies should use a regional average emission factor for electricity to avoid double counting”.
“Companies with any operations in markets providing product or supplier-specific data in the form of contractual instruments shall report scope 2 emissions in two ways and label each result according to the method: one based on the location-based method, and one based on the market-based method. This is also termed ‘dual reporting’”.
“Offsets shall not be included in the impact assessment of a PEF study, but may be reported separately as additional environmental information”.
“Where electricity is used for the production of renewable liquid and gaseous fuels of non-biological origin the average share of electricity from renewable sources in the country of production, as measured two years before the year in question, shall be used to determine the share of renewable energy. However, electricity obtained from direct (i.e., physical) connection to an installation generating renewable electricity may be fully counted as renewable electricity provided that the installation (a) comes into operation after or at the same time as the installation producing… and (b) is not connected to the grid or is connected to the grid but no electricity taken from it…”
“The Commission should develop, by means of delegated acts, a reliable Union methodology to be applied where such electricity (of renewable origin) is taken from the grid. That methodology should ensure that there is a temporal and geographical correlation between the electricity production unit with which the producer has a bilateral renewables power purchase agreement … Furthermore, there should be an element of additionally, meaning that the fuel producer is adding to the renewable deployment or to the financing of renewable energy”.
3. Results of the Analyses
3.1. Inaccuracy of LCI Data in Existing Leading Databases
3.2. Outdated Data in a Rapid Changing World
- The reference year in ELCD V3.2 is 2008;
- The reference year in Ecoinvent V3.5 is 2014;
- The reference year in PEF (GaBi) is 2013.
3.3. Fluctuations Over Time and Time-Related Allocation
3.3.1. Fluctuation Over Time (Short Term)
“Electricity markets are relatively difficult to delimit, given the internationally connected grids. In addition and related to the time-representativeness, it matters whether the named consumer good would be operated only at peak hours (e.g., an electric toothbrush) or continuously (e.g., a fridge) or only during night time at base load (e.g., an electric storage heater)”.
3.3.2. Time-Related Allocation of the Environmental Burden of Windmill and PV Cells
“Reductions in indirect emissions (changes in scope 2 or 3 emissions over time) may not always capture the actual emissions reduction accurately. This is because there is not always a direct cause-effect relationship between the activity of the reporting company and the resulting GHG emissions. For example, a reduction in air travel would reduce a company’s scope 3 emissions. This reduction is usually quantified based on an average emission factor of fuel use per passenger. However, how this reduction actually translates into a change in GHG emissions to the atmosphere would depend on a number of factors, including whether another person takes the “empty seat” or whether this unused seat contributes to reduced air traffic over the longer term. Similarly, reductions in scope 2 emissions calculated with an average grid emissions factor may over- or underestimate the actual reduction depending on the nature of the grid”.
- a = time-based economic allocated environmental burden per kWh;
- x = yearly average environmental burden of electricity per kWh of a windmill or PV cell (as available in background process databases, e.g., Ecoinvent, GaBi, Idemat, ProBas);
- z(t) = time-based economic allocation factor = ‘actual price per kWh’ divided by the ‘yearly average price per kWh’;
- t = time in discrete steps of 1 h.
3.3.3. Time-Related Allocation of the Environmental Burden of the Electricity Mix at the Grid
- a = time-based physical allocated environmental burden per kWh;
- x = yearly average environmental burden of electricity per kWh of the country consumption (or production) mix (as available in background process databases, e.g., Ecoinvent, Gabi, Idemat, ProBas);
- c(t) = time-based physical allocation factor = ‘actual carbon content in kg CO2eq per kWh’ divided by the ‘yearly average carbon content in kg CO2eq per kWh’. Note that this ratio is a mass-based allocation factor;
- t = time in discrete steps of 1 h.
3.4. The Guarantee of Origin (GO) and the Residual Mix
“For the purpose of this Directive […] ‘guarantee of origin’ means an electronic document which has the sole function of providing evidence to a final customer that a given share or quantity of energy was produced from renewable sources. A guarantee of origin can be transferred, independently of the energy to which it relates, from one holder to another”.
“The Corporate Standard does not address potential double counting between consumers of emissions associated with green power instruments.” And “implementing a credible and robust system for GHG emission… would require that only one consumer reports the emissions from a given quantity of generation”.
3.5. Selection of Midpoints or Endpoint Systems: The Specific Case of Nuclear Energy
“The selection of impact categories, category indicators and characterization models shall be both justified and consistent with the goal and scope of the LCA. The selection of impact categories shall reflect a comprehensive set of environmental issues related to the product system being studied, taking the goal and scope into consideration.”
- In endpoint systems there is no single truth: ReCiPe is based on damage, EF is primarily based on normalization and weighting by a hybrid system of public opinion and expert panels, and eco-costs is based on prevention (i.e., 3 different approaches to weighting in situations of trade-off). It is a coincidence that the eco-costs system and EF have nearly the same ranking (only the positions of DK, FR, and BE switch);
- Data of countries can change rapidly: in the period 2013–2016, the carbon intensity improved in GB by 38%, in DK by 34%, where the ENTSO-E data improved by 11% (see also Figure 3). Therefore, the ranking of countries changes quickly and might be affected by the age of the LCI database;
- Common practice in LCA is to take one database and one endpoint system as the basis for a comparison. Combining two different databases in one benchmark study is not advised, since it adds an extra level of inaccuracy, especially for country scores. On the other hand, it is advised to calculate the LCA results with two or more Life Cycle Impact Assessments (LCIA) methods in order to assess the robustness of the conclusions.
4. Discussion
- RQ 1. What is the uncertainty in applying LCI data from existing leading databases?
- RQ 2. What is the effect of the fact that the LCI data are in practice 4–8 years old on average?
- RQ 3. How to deal with the fluctuations over time of the carbon intensity of electricity and time-related allocation on a time scale of 1 h?
- RQ 4. When does it make sense to use in LCA electricity Guarantee of Origin and residual mix, and when the consumption mix?
- RQ 5. How to deal with nuclear power in relation to greenhouse gas emissions (the issue of comparing ‘apples with oranges’ by the selection of midpoints and endpoints in LCA)?
5. Conclusions
- In ex-post generic LCAs on battery electric vehicle systems and electrolytic hydrogen systems, the use of the country consumption mix (in contrast to the EU grid mix) goes hand in hand with high inaccuracies, see Table 1. Sensitivities analyses might help assessing the robustness/variability of the conclusions.
- The residual mix should not be applied because of its huge inaccuracies. Only ‘bundled GOs’ should be used in LCA, taking into account the required electricity storage.
- Ex-ante LCA studies should refrain from using the country consumption mix (ex-post) data from databases, but should always have an assumed future technical mix of electricity (percent wind power, solar power, etcetera, and fossil fuels), and have a sensitivity analysis on it. Note that the vast majority of published LCA studies are ex-ante, since they aim at mitigation of environmental pollution by redesigning the product-service systems for the future.
- New time-dependent allocation rules are needed in LCA benchmarking of modern technologies like battery electric vehicle systems and hydrogen fuel cell vehicle systems, to cope with the fact that these new technologies are designed to partly use electricity at moments of overproduction of, e.g., wind farms.
- There is a rather urgent need for a new electricity database, that is built on bottom-up data like the European Pollutant Release and Transfer Register (E-PRTR) [26]. Such a database can provide an accurate assessment of the emissions from the production mix per country. Combined with import and export data from ENTSO-E, country consumption mix data can be calculated which are not older than 2 years. In such a way, the LCI lists will be much more accurate, since they are based on measurements, and calculations are based on the locally applied technologies and specific feedstock data. The database should be open access and transparent.
- (1)
- The electricity calculation rules in the PEF project should be reconsidered;
- (2)
- The GHG protocol should distinguish between bundled and unbundled RECs and GOs;
- (3)
- The EU should stimulate ‘full disclosure’ systems of electricity in member states;
- (4)
Author Contributions
Funding
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A. The Basics of Time-Related Allocation; Production of Hydrogen by Electrolysis as an Example
- The products of a sheep: which part of the eco-burden of a sheep (cradle-to-gate of its primary products) is allocated to the wool and which part is allocated to the meat (and the leather)?
- The products of a tree: which part of the eco-burden of a harvested tree (cradle-to gate of its primary products) is allocated to the wooden beams, and which part to the wood chips (and saw dust)?
- Multiplying the yearly average environmental burden of the electricity of a windmill (or PV cell), as provided in databases on background processes, with an economic allocation factor (i.e., the ‘actual price per kWh’ divided by the ‘yearly average price per kWh’).The reason to apply economic allocation is that it is considered unfair that the low-valued electricity at moments of overproduction, or nearly overproduction, carry the same eco-burden than high-valued electricity. Note that such an allocation factor is smaller than 1 in periods where the demand of electricity is low, and higher than 1 when the demand of electricity is high.
- Multiplying the yearly average environmental burden of the electricity mix in a country, as provided in databases on background processes, with a physical allocation factor (i.e., the ‘actual carbon content per kWh’ divided by the ‘yearly average carbon content per kWh’).Note that the actual carbon content ratio provides a better allocation factor for the country electricity mix than the price ratio, since the price and the carbon content have a weak relationship because of fluctuations in the feedstock prices of fossil fuels in the electricity mix.
- a = time-based economic allocated environmental burden per kWh;
- x = yearly average environmental burden of electricity per kWh of a windmill or PV cell (as available in background process databases, e.g., Ecoinvent, GaBi, Idemat, ProBas);
- y(t) = , where electricity(t) indicate the power in kWh in function of the time t;
- z(t) = time-based economic allocation factor = ‘actual price per kWh’ divided by the ‘yearly average price per kWh’;
- t = time in discrete steps of 1 h.
- a = time-based physical allocated environmental burden per kWh;
- x = yearly average environmental burden of electricity per kWh of the country consumer (or production) mix (as available in background process databases, e.g., Ecoinvent, Gabi, Idemat, ProBas);
- y(t) = , where electricity(t) indicate the power in kWh in function of the time t;
- c(t) = time-based physical allocation factor = ‘actual carbon content in kg CO2eq/ kWh’ divided by the ‘yearly average carbon content in kg CO2eq/ kWh’;
- t = time in discrete steps of 1 h.
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a | b | c | d | e | f | g | h | i | |
---|---|---|---|---|---|---|---|---|---|
Carbon Intensity (gCO2eq/MJ) | IEA incl. Upstream (2013) [11] | Ecoinvent V3.5 Consumption Mix (2014) | ELCD V3.2 Consumption Mix (2008) | PEF (Gabi) Consumption Mix (2013) | (b−a)/a (%) | (c−a)/a (%) | (d−a)/a (%) | PEF (GaBi) Residual Mix (2013) | (h−a)/a (%) |
BE | 73 | 75 | 74 | 67 | 3% | 2% | −8% | 40 | −45% |
DE | 167 | 177 | 168 | 166 | 6% | 0% | −1% | 217 | 30% |
DK | 102 | 101 | 129 | 84 | −1% | 27% | −18% | 176 | 73% |
ES | 90 | 95 | 122 | 114 | 5% | 35% | 26% | 122 | 35% |
FR | 28 | 15 | 29 | 26 | −47% | 3% | −7% | 22 | −22% |
GB | 166 | 147 | 157 | 153 | −12% | −6% | −8% | 158 | −5% |
NL | 155 | 174 | 145 | 141 | 12% | −6% | −9% | 149 | −4% |
PL | 263 | 285 | 288 | 278 | 8% | 10% | 6% | 293 | 12% |
SE | 13 | 12 | 15 | 12 | −6% | 17% | −6% | 55 | 330% |
ENTSO-E | 120 | 116 | 131 | 118 | −3% | 9% | −2% | 136 | 13% |
Carbon footprint [11] | SE (11%) | FR (23%) | BE (61%} | ES (75%) | DK (85%) | ENTSO-E (100%) | NL (129%) | GB (139%) | DE (139%) | PL (219%) |
ReCiPe2016 + EI | SE (11%) | FR (14%) | BE (45%) | DK (70%) | GB (77%) | NL (100%) | ENTSO-E (100%) | ES (103%) | DE (116%) | PL (251%) |
EF + PEF (GaBi) | SE (33%) | DK (57%) | BE (73%) | FR (83%) | NL (93%) | ES (100%) | ENTSO-E (100%) | DE (109%) | GB (119%) | PL (190%) |
EF + EI | SE (39%) | FR (59%) | BE (74%) | DK (77%) | ES (90%) | ENTSO-E (100%) | NL (106%) | GB (118%) | DE (132%) | PL (202%) |
Eco-costs2017 + EI | SE (39%) | DK (70%) | FR (74%) | BE (81%) | ES (93%) | ENTSO-E (100%) | NL (104%) | GB (116%) | DE (133%) | PL (209%) |
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Olindo, R.; Schmitt, N.; Vogtländer, J. Life Cycle Assessments on Battery Electric Vehicles and Electrolytic Hydrogen: The Need for Calculation Rules and Better Databases on Electricity. Sustainability 2021, 13, 5250. https://doi.org/10.3390/su13095250
Olindo R, Schmitt N, Vogtländer J. Life Cycle Assessments on Battery Electric Vehicles and Electrolytic Hydrogen: The Need for Calculation Rules and Better Databases on Electricity. Sustainability. 2021; 13(9):5250. https://doi.org/10.3390/su13095250
Chicago/Turabian StyleOlindo, Roberta, Nathalie Schmitt, and Joost Vogtländer. 2021. "Life Cycle Assessments on Battery Electric Vehicles and Electrolytic Hydrogen: The Need for Calculation Rules and Better Databases on Electricity" Sustainability 13, no. 9: 5250. https://doi.org/10.3390/su13095250