Student exercise 3: Prospective Environmental Assessment Objectives The goal in the first two exercises was to determine scenarios for future e-bike mobility in Europe until 2050 and quantify implications on material demand. More specifically, the first exercise on formative scenario analysis (FSA) provided insights about the market penetration of e-bikes and battery technologies (technology), the implementation and adaptation of e-biking mobility among consumers and in society (consumption pattern) and the way the adoption of e- bikes changes the mobility behavior and the modes of transport that are being replaced (substitution). In the subsequent second exercise on dynamic material flow analysis (MFA), you modelled the implications of a potential transition to e- bike mobility in terms of material stocks and flows. The goal of the third and final exercise is to assess the environmental impacts and benefits (here: greenhouse gas emissions, GHG) induced by the future use of e-bikes. Your objectives for this third exercise are to: Calculate the environmental impacts (in terms of GHG) of e-bike mobility as a function of time for your scenarios. Discuss the feasibility and the environmental benefits and impacts (in terms of GHG) of future material recycling of lithium. Put the resulting environmental impacts into a system-wide perspective and assess the impacts of personal mobility scenarios (from Exercise 1), with a focus on the comparison of GHG emissions caused by e-bike mobility and the modes of transport that are being substituted. Consider the effect of experience on environmental impacts (upscaling and learning). Apply discounting to see how it affects your results and how it may influence your conclusions. The exercise is structured into three parts focusing on the environmental assessment of the e-bike market (Part I), the overall impact of mobility and the changes induced by e-bike mobility (Part II), and finally discounting impacts in the dynamic LCA (Part III). Please provide the following three items in your solution: 1. Greenhouse gas emissions (in CO 2 -equivalents) due to future e-bike mobility. 2. Substituted greenhouse gas emissions (in CO 2 -equivalents) due to reductions of other means of transport and taking into consideration technology development. 3. Potential reductions in GHG emissions (in CO 2 -equivalents) due to discounting. 1
Part I Environmental impacts of future e-bike mobility Use your assumptions on e-bikes sold, kilometres driven, and battery replacements to determine the inventory of your system until 2050. From the kilometres driven you can derive the electricity that is consumed (for typical electricity consumption per km see Table 2). GHG emissions for various ways to produce electricity are given in Table 3. For the inventory and GHG emissions of one unit of e-bike, you can use ecoinvent 3.3. To log into ecoinvent, please use the following credentials: Website: https://ecoquery.ecoinvent.org/account/logon User: studentethz Password: go-ecoinvent2017 Please make sure the ecoinvent LCI datasets are consistent with your own assumptions (e.g. weight of the battery) and correct them if necessary. For the assessment of GHG, use the IPCC 2013 GWP100 method. Which life-cycle stages are important for future e-bike mobility and what are sensitive parameters? Optionally, if you assessed various battery technologies in Exercise 2, compare these and the overall influence on GHG impact results. Based on exercise 2, discuss potential gains in terms of future lithium recycling in your scenarios. A new recycling process is becoming available, which is able to save 2.5 kg CO 2 -eq per 1.0 kg LiMn 2 O 4 (Dunn et al. 2012a). Dunn et al. 2012a and 2012b assume that in the direct physical process 95% of LiMn 2 O 4 can be recovered and therefore directly substitute primary material. Since lithium recycling is a new technology, experience effects (learning and upscaling) can be expected. Assume an experience index of b=0.6 and a current cumulative recycling of 5 000 tonnes of lithium batteries in the EU. Will lithium recycling improve the GHG impacts in your scenarios and is this improvement relevant with regard to overall GHG emissions of e-bike mobility? Part II Environmental impact of overall mobility In exercise 1 you quantified the substitution of other modes of transport (e.g. passenger car or public transport) by e-bike mobility. Assess the GHG impact savings induced by this reduction in other modes of transportation in your scenarios. Make sure that you select coherent system boundaries. If you have not quantified substitution in exercise 1, make the according assumptions here and document them shortly. You find inventory data for various personal car technologies in Tables 2 and 3, for the current situation and 2030. As you can see, the technologies are projected to improve in terms of mass and energy efficiency (experience effect). Extrapolate the trajectories to 2050 by assuming that the two data points for the current situation and 2030 lie on the experience curve. All gliders scale with cumulative of car gliders (independent of car technology). This is different for the battery and the generator/motors for BEV, which scale with cumulative of BEV. If you do not have any other estimates from Exercise 1, assume that until 2
2030, 200 million new vehicles will be produced in Europe, of which 6% are BEV, 10% ICEV- c, 44% ICEV-p and 40% ICEV-g. Would you expect that experience (learning and upscaling) also influences the environmental impact of e-bike mobility? Discuss qualitatively why or why not. Determine the greenhouse gas emissions of the future transport system and the substitution effect of future e-bike use. Discuss the environmental impacts caused by the rebound effect (based on exercise 1). Discuss qualitatively whether you expect a change in the results, if you would use the Global Temperature Change Potentials 100 years (GTP 100 ) instead of GWP 100 as characterization factors. Part III Discounting Quantify the difference in greenhouse gas emissions if discounting is applied to your prospective scenario(s). Do a sensitivity analysis for the discount rate and provide a brief discussion of the arguments for and against temporal discounting of environmental impacts. For all three parts, please discuss the implications of your findings and discuss what policy recommendations could be derived. For the executive summary In addition to the above tasks, please discuss which factors and assumptions you expect to have the largest contribution to the uncertainty of your results. Propose possible strategies for decreasing the uncertainty. Organisational issues The exercise should be submitted to heeren@ifu.baug.ethz.ch before the May 16th. If you are interested in receiving feedback, please submit an executive summary of less than 5 pages and your calculation (preferably in Excel format). The executive summary shall be a standalone document, containing all necessary figures to understand the results and interpretation. The files should be named with the surnames of the group members and the exercise number (e.g. PEA 2016_Ex3_Müller_Meyer ). You may also refer to the above email address should you have questions regarding the exercise All documents, the lecture slides, and further reading on the prospective assessment of mobility are available on the course website 3
Table 1 Mass of the main components for different vehicle technologies in two different reference years (2012 and 2030), based on information provided in Bauer et al. (2015). Abbreviations: ICEV: internal combustion engine vehicle; BEV: battery electric vehicle, FCEV: fuel-cell electric vehicle, -g: gasoline, -d: diesel, and c: compressed natural gas (CNG). Mass, in [kg] Glider Technology/year (body & chassis) Tank Powertrain (motor/generator/engine/transmission) Battery ICEV-g currently 1195 86 261 0 2030 1080 69 206 0 ICEV-d currently 1195 76 285 0 2030 1080 63 224 0 currently 1195 117 175 0 2030 1080 112 218 0 BEV currently 1195 0 233 448 2030 1080 0 171 327 Table 2 Energy (fuel) consumption of different vehicle technologies for two reference years (2012 and 2030. Abbreviations: ICEV: internal combustion engine vehicle; BEV: battery electric vehicle, FCEV: fuel-cell electric vehicle, -g: gasoline, -d: diesel, and c: compressed natural gas (CNG).The energy future consumption of the e-bike depends on the development of the weight. Technology Year Energy (fuel) consumption [MJ/km] ICEV-g currently 2.80 2030 2.17 ICEV-d currently 2.43 2030 1.93 currently 2.75 2030 2.14 BEV currently 0.91 2030 0.75 e-bike currently 0.036* 2030? *refers to an e-bike of 24 kg weight. 4
Table 3 Aggregated life cycle impact assessment (LCIA) scores of vehicle components (+ end-of-life, EoL), energy (fuel) supply, and exhaust emissions in terms of climate change impacts (using global warming potential GWP 100 as characterization factors; in [kg-co 2 -eq.]) adapted from information provided in Bauer et al. (2015). Grey fields indicate no change over time, i.e. parameter assumed constant. Powertrain: motor/generator/engine + transmission. Vehicle type Inventory Unit Currently [kg-co 2 -eq.] 2050 [kg-co 2 -eq.] All Glider, with EOL [kg] 6.0 6.0 ICEV Passenger car, ICE powertrain, with EoL [kg] 3.24 3.24 BEV Passenger car, electric powertrain (w/o battery), with EoL [kg] 9.00 9.00 BEV Battery, Lithium-ion, (EoL negligible) [kg] 5.0 5.0 Fuel tank, natural gas, 250 bar, with EoL [kg] 2.75 2.75 ICEV-g Exhaust emissions, gasoline, per kg fuel consumed [kg] 3.22 3.22 ICEV-d Exhaust emissions, diesel, per kg fuel consumed [kg] 3.15 3.15 Exhaust emissions, natural gas, per kg fuel consumed [kg] 2.66 2.66 Electricity, low voltage, average European consumption mix [kwh] 0.59 0.30 Electricity, low voltage, wind power [kwh] 0.03 0.027 Electricity, low voltage, photovoltaic (PV) power [kwh] 0.09 0.04 Electricity, low voltage, hydro power [kwh] 0.01 0.01 Electricity, low voltage, combined-cycle gas-fired power [kwh] 0.49 0.45 Electricity, low voltage, average European coal-fired power [kwh] 1.22 0.83 Electricity, low voltage, nuclear power [kwh] 1.59 1.59 ICEV-g Petrol (gasoline), low-sulphur, at service station [kg] 0.79 0.79 ICEV-d Diesel, low-sulphur, at service station [kg] 0.60 0.60 Natural gas, for high-pressure network (1-5 bar), at service station [kg] 0.59 0.59 Further reading and references Dunn, J. B., Gaines, L., Sullivan, J., & Wang, M. Q. (2012). Impact of recycling on cradle-to-gate energy consumption and greenhouse gas emissions of automotive lithium-ion batteries. Environmental Science and Technology, 46(22), 12704 12710. http://doi.org/10.1021/es302420z Bauer, C., Hofer, J., Althaus, H.-J., Del Duce, A., & Simons, A. (2015). The environmental performance of current and future passenger vehicles: Life cycle assessment based on a novel scenario analysis framework. Applied Energy, 157, 871 883. http://doi.org/10.1016/j.apenergy.2015.01.019 5