Supplementary Material to
“Transport electrification:
a key element for energy system transformation and climate stabilization”
inClimatic Change
David McCollum1#*, Volker Krey1#, Peter Kolp1, Yu Nagai1, Keywan Riahi1
1International Institute for Applied Systems Analysis, Laxenburg2361, Austria.
#D.M. and V.K. contributed equally to this work.
*e-mail:
Table of Contents
1.Brief description of the MESSAGE-MACRO integrated assessment modeling framework
2.Brief description of the MAGICC reduced-complexity global climate model
3.Brief description of the stylized transport module in MESSAGE-MACRO
4.Assumptions for electric vehicle stock calculations in the main text
5.Feedstock CO2 emissions sensitivity analysis
6.Measuring diversity through the Shannon-Wiener index
7.Further details on mitigation costs and scenario feasibility
References
1.Brief description of the MESSAGE-MACRO integrated assessment modeling framework
The MESSAGE (Model for Energy Supply Strategy Alternatives and their General Environmental Impact) integrated assessment model (IAM) is a global systems engineering optimization model used for medium- to long-term energy system planning, energy policy analysis, and scenario development(Messner and Strubegger 1995; Riahi et al. 2012; van Vliet et al. 2012). Developed at the International Institute for Applied Systems Analysis (IIASA) for more than two decades, MESSAGE is an evolving framework that, like other global IAMs in its class (e.g., MERGE, ReMIND, IMAGE, WITCH, GCAM, etc.), has gained wide recognition over time through its repeated utilization in developing global energy and emissions scenarios (e.g., Nakicenovic and Swart(2000)).
The MESSAGE model divides the world up into eleven (11) regions (Supplementary Figure 1, Supplementary Table 1) in an attempt to represent the global energy system in a simplified way, yet with many of its complex interdependencies, from resource extraction, imports and exports, conversion, transport, and distribution, to the provision of energy end-use services such as light, space conditioning, industrial production processes, and transportation. Trade flows (imports and exports) between regions are monitored, capital investments and retirements are made, fuels are consumed, and emissions are generated. In addition to the energy system, the model includes also the other main greenhouse-gas emitting sectors, agriculture and forestry. MESSAGE tracks a full basket of greenhouse gases and other radiatively active gases – CO2 , CH4 , N2O , NOx , volatile organic compounds (VOCs), CO, SO2, PM, BC, OC, NH3, CF4, C2F6, HFC125, HFC134a, HFC143a, HFC227ea, HFC245ca, and SF6 – from both the energy and non-energy sectors (e.g., deforestation, livestock, municipal solid waste, manure management, rice cultivation, wastewater, and crop residue burning). In other words, all Kyoto gases plus several others are accounted for.
Supplementary Figure 1. Map of 11 regions in MESSAGE model
Supplementary Table 1. Listing of 11 MESSAGE regions by country
11 MESSAGE regions / Definition (list of countries)NAM / North America
(Canada, Guam, Puerto Rico, United States of America, Virgin Islands)
WEU / Western Europe
(Andorra, Austria, Azores, Belgium, Canary Islands, Channel Islands, Cyprus, Denmark, Faeroe Islands, Finland, France, Germany, Gibraltar, Greece, Greenland, Iceland, Ireland, Isle of Man, Italy, Liechtenstein, Luxembourg, Madeira, Malta, Monaco, Netherlands, Norway, Portugal, Spain, Sweden, Switzerland, Turkey, United Kingdom)
PAO / Pacific OECD
(Australia, Japan, New Zealand)
EEU / Central and Eastern Europe
(Albania, Bosnia and Herzegovina, Bulgaria, Croatia, Czech Republic, The former Yugoslav Rep. of Macedonia, Hungary, Poland, Romania, Slovak Republic, Slovenia, Estonia, Latvia, Lithuania)
FSU / Former Soviet Union
(Armenia, Azerbaijan, Belarus, Georgia, Kazakhstan, Kyrgyzstan, Republic of Moldova, Russian Federation, Tajikistan, Turkmenistan, Ukraine, Uzbekistan)
CPA / Centrally Planned Asia and China
(Cambodia, China (incl. Hong Kong), Korea (DPR), Laos (PDR), Mongolia, Viet Nam)
SAS / South Asia
(Afghanistan, Bangladesh, Bhutan, India, Maldives, Nepal, Pakistan, Sri Lanka)
PAS / Other Pacific Asia
(American Samoa, Brunei Darussalam, Fiji, French Polynesia, Gilbert-Kiribati, Indonesia, Malaysia, Myanmar, New Caledonia, Papua, New Guinea, Philippines, Republic of Korea, Singapore, Solomon Islands, Taiwan (China), Thailand, Tonga, Vanuatu, Western Samoa)
MEA / Middle East and North Africa
(Algeria, Bahrain, Egypt (Arab Republic), Iraq, Iran (Islamic Republic), Israel, Jordan, Kuwait, Lebanon, Libya/SPLAJ, Morocco, Oman, Qatar, Saudi Arabia, Sudan, Syria (Arab Republic), Tunisia, United Arab Emirates, Yemen)
LAC / Latin America and the Caribbean
(Antigua and Barbuda, Argentina, Bahamas, Barbados, Belize, Bermuda, Bolivia, Brazil, Chile, Colombia, Costa Rica, Cuba, Dominica, Dominican Republic, Ecuador, El Salvador, French Guyana, Grenada, Guadeloupe, Guatemala, Guyana, Haiti, Honduras, Jamaica, Martinique, Mexico, Netherlands Antilles, Nicaragua, Panama, Paraguay, Peru, Saint Kitts and Nevis, Santa Lucia, Saint Vincent and the Grenadines, Suriname, Trinidad and Tobago, Uruguay, Venezuela)
AFR / Sub-Saharan Africa
(Angola, Benin, Botswana, British Indian Ocean Territory, Burkina Faso, Burundi, Cameroon, Cape Verde, Central African Republic, Chad, Comoros, Cote d'Ivoire, Congo, Democratic Republic of Congo, Djibouti, Equatorial Guinea, Eritrea, Ethiopia, Gabon, Gambia, Ghana, Guinea, Guinea-Bissau, Kenya, Lesotho, Liberia, Madagascar, Malawi, Mali, Mauritania, Mauritius, Mozambique, Namibia, Niger, Nigeria, Reunion, Rwanda, Sao Tome and Principe, Senegal, Seychelles, Sierra Leone, Somalia, South Africa, Saint Helena, Swaziland, Tanzania, Togo, Uganda, Zambia, Zimbabwe)
A typical model application is constructed by specifying performance characteristics of a set of technologies and defining a Reference Energy System (RES) that includes all the possible energy chains that MESSAGE can make use of. In the course of a model run, MESSAGE determines how much of the available technologies and resources are actually used to satisfy a particular end-use demand, subject to various constraints (both technological and policy), while minimizing total discounted energy system costsover the entire model time horizon (1990-2110). It does this based on a linear programming, optimizationsolution algorithm.The representation of the energy system includes vintaging of the long-lived energy infrastructure, which allows for consideration of the timing of technology diffusion and substitution, the inertia of the system for replacing existing facilities with new generation systems, clustering effects (technological interdependence) and – in certain versions of the model – the phenomena of increasing returns (i.e., the more a technology is applied the more it improves and widens its market potentials). Combined, these factors can lead to “lock-in” effects (Arthur 1989; Arthur 1994) and path dependency (change occurs in a persistent direction based on an accumulation of past decisions). As a result, technological change can go in multiple directions, but once change is initiated in a particular direction, it becomes increasingly difficult to alterits course.
Important inputs for MESSAGE are technology costs and technology performance parameters (e.g., efficiencies and investment, variable, and O&M costs). For the scenarios included in this paper, technical, economic and environmental parameters for over 100 energy technologies are specified explicitly in the model. Costs of technologies are assumed to decrease over time as experience (measured as a function of cumulative output) is gained. For assumptions concerning the main energy conversion technologies see the following references: Riahi et al. (2007), Nakicenovic and Swart (2000), Riahi et al.(2012), and van Vliet et al. (2012). For information on carbon capture and storage technologies specifically, seeRiahi et al.(2004).
MESSAGE is able to choose between both conventional and non-conventional technologies and fuels (e.g., advanced fossil, nuclear fission, biomass, and renewables), and in this respect the portfolio of technologies/fuels available to the model obviously has an important effect on the model result. In the version of the model used in this study, we consider a portfolio of technologies whose components are either in the early demonstration or commercialization phase (e.g., coal, natural gas, oil, nuclear, biomass, solar, wind, hydro, geothermal, carbon capture and storage, hydrogen, biofuels, and electrified transport, to name just a subset). Notably, this portfolio includes bio-CCS, a technology that can potentially lead to negative emissions (i.e., permanent underground storage of CO2 which was originally pulled out of the atmosphere by photosynthesis). Exceedingly futuristic technological options, such as nuclear fusion and geo-engineering,are, however, not considered.
Other important input parameters for our modeling include fossil fuel resource estimates and potentials for renewable energy. For fossil fuel availability, the model distinguishes between conventional and unconventional resources for eight different categories of (oil, gas, coal) occurrences (Riahi et al. 2012; Rogner 1997). For renewable potentials we rely on spatially explicit analysis of biomass availability and adopt the assumptions discussed inRiahi et al.(2012).
Price-induced changes in energy demand (i.e., elastic demands) are also modeled in MESSAGE via an iterative link to MACRO, a top-down, macro-economic model of the global economy(Messner and Schrattenholzer 2000). Through an iterative solution process, MESSAGE and MACRO exchange information on energy prices, energy demands, and energy system costs until the demand responses are such (for each of the six end-use demand categories in the model: electric and thermal heat demands in the industrial, residential, commercial, and transportation sectors)that the two models have reached equilibrium. This process is parameterized off of a baseline scenario (which assumes some autonomous rate of energy efficiency improvement, AEEI) and is conducted for all eleven MESSAGE regions simultaneously. Therefore, the demand responses motivated by MACRO are meant to represent the additional (compared to the baseline) energy efficiency improvements and conservation that would occur in each region as a result ofhigher prices for energy services. The macro-economic response captures both technological and behavioral measures (at a high level of aggregation), while considering the substitutability of capital, labor, and energy as inputs to the production function at the macro level.
Further and more detailed information on the MESSAGE modeling framework is available, including documentation of model set-up and mathematical formulation(Messner and Strubegger 1995; Riahi et al. 2012) and the model’s representation of technological change and learning(Rao et al. 2006; Riahi et al. 2004; Roehrl and Riahi 2000).
2.Brief description of theMAGICC reduced-complexity global climate model
MAGICC (Model for the Assessment of Greenhouse-gas Induced Climate Change), version 5.3, has been used in this study to estimate the climate system impacts of the varying greenhouse gas emission trajectories of the scenarios in the ensemble. MAGICC is a reduced-complexity coupled global climate-carbon cycle model, in the form of a user-friendly software package that runs on a personal computer(Wigley 2008). In its standard form, MAGICC calculates internally consistent projections for atmospheric concentrations, radiative forcing, global annual-mean surface air temperature, ice melt, and sea level rise, given emissions trajectories of a range of gases (CO2, CH4, N2O, CO, NOx, VOCs, SO2, and various halocarbons, including HCFCs, HFCs, PFCs, and SF6), all of which are outputs from MESSAGE. The time horizon of the model extends as far back as 1750 and can make projections as far forward as 2400. The climate model in MAGICC is an upwelling-diffusion, energy-balance model, which produces output for global- and hemispheric-mean temperature and for oceanic thermal expansion. Climate feedbacks on the global carbon cycle are accounted for through the interactive coupling of the climate model and a range of gas-cycle models. MAGICC has been used in all IPCC Assessment reports, dating back to 1990, and its strength lies in its ability to replicate the more complex global climate models that run on supercomputers. For our analysis, we use a version of the software that is consistent with the IPCC Fourth Assessment Report, Working Group 1, except that the model has been slightly modified to permit the explicit treatment of black and organic carbon (BC and OC) and their impacts on the global climate.[1]
The 550 ppm and the 450 ppm CO2-eq climate targets adopted by the EMF27 modeling protocol are implemented based on the increase in radiative forcing (RF) – from all greenhouse gases and forcing agents, excluding contributions from albedo change, nitrate aerosols, and mineral dust – compared to the pre-industrial era (1750). CO2-eq concentrations are then calculated from radiative forcing using the standard approximation formula: C0exp(RF/α), where C0 = 278 ppm and α=5.35.
3.Brief description of the stylized transport module in MESSAGE-MACRO
The version ofMESSAGE-MACRO employed in this study includes a quite stylized representation of the transport sector that essentially captures only fuel switching and price-elastic demandsas mechanisms to respond to climate and energy policies. The following brief description elaborates the main characteristics of thistransport module.
The model chooses between different final energy forms to provide useful energy for transportation. This decision is based primarily on the energy service costs by fuel, taking into account fuel prices at the final energy level and the respective final-to-useful energy conversion efficiencies. (In addition, “inconvenience” or “disutility” costs are applied to non-liquid fuels, in order to capture market adoption hurdles that MESSAGE-MACRO is not equipped to handle in its current form.) These conversion efficiencies vary by energy carrier. Useful energy demands (for the aggregate transportation sector of each region) are first specified in terms of ICE-equivalent, which therefore by definition have a conversion efficiency of final to useful energy of 1. Relative to that, the conversion efficiency of alternative fuels is higher, for example electricity in 2010 has a factor of ~3x higher final-to-useful efficiency than the regular oil-product based ICE. The assumed efficiency improvements of the ICE vehiclesin the transportation sector, as well asmode-switching and other behavioral changes, are implicitly embedded in the baseline demand specifications. These come from the MESSAGE scenario generator[2] (seeRiahi et al. (2007) for more information).
Additional demand reduction in response to price increases (e.g., in policy scenarios) then occurs via two mechanisms: (i) the fuel switching option (due to the fuel-specific relative efficiencies), and (ii) the linkage with the macro-economic model MACRO (see Section 1). Supplementary Figure 2 graphically illustrates the main components of the stylized transport sector representation in MESSAGE-MACRO.
Supplementary Figure 2. Schematic diagram of the stylized transport sector representation in MESSAGE-MACRO
To reflect limitations of switching to alternative fuels, for example as a result of limited infrastructure availability (e.g., rail network) or some energy carriers being largely unsuitable for certain transport modes (e.g., electrification of aviation), share constraints are imposed on certain energy carriers (e.g., electricity) and energy carrier groups (e.g., liquid fuels) of the transport sector. In addition, the diffusion speed of alternative fuels is limited to mimic known bottlenecks in the supply chain, particularly those not explicitly represented in MESSAGE (e.g., non-energy related infrastructure). Both the share and diffusion constraints are typically parameterized based on transport sector studies that analyze such developments and their feasibility in much greater detail – the current paper being a prime example of this.
The cost-markup of the different fuel options in MESSAGE-MACRO can essentially be interpreted as incremental annualized non-fuel life-cycle costs of vehicles compared a reference technology (e.g., ICE vehicle). However, by making a fewreasonable assumptions, this cost-markup can also be interpreted as a combination of incremental investment and operation and maintenance (O&M) costs. Reporting of this metric, particularly for BEVs, may help in the interpretation of the results and when comparing with other studies that employ an explicit representation of vehicle technologies.
While investment costs tend to be higher for BEVs than for conventional ICEs, a number of studies indicate that O&M costs (excluding fuel costs) for BEVs are actually lower by some 35-40%(Diez 2012; OTT 2002). Assuming O&M costs for ICE cars (depending on vehicle size/type and country) in the range of 500-1000 US$2005 per year(OTT 2002; RACV 2013), a vehicle lifetime of 15 years, a discount rate of 5%/yr and annual mileage of 10,000 to 20,000 km/vehicle/yr (as also assumed in Section 4 below), the resulting investment cost increment for BEVs compared to ICE vehicles in the 2020 period amounts to about 1950 to 4450 US$2005. Beyond 2020 we do not assume any further decrease in the cost increment. However, the variation of the upper bound on the electrification rate (i.e., the constraint modified in our “ET” sensitivity analysis) is consistent with the assumption that for the same cost-markup the vehicle range of BEVs varies. For example, the high electrification scenario ET75% implies that range limitations at the above cost increment (relative to ICE vehicles) are essentially not an issue, and therefore the light duty vehicle fleet can be almost completely electrified.
Finally, the demand for international shipping is modeled in a very simple way with a number of different energy carrier options (light and heavy fuel oil, biofuels, natural gas, and hydrogen).Demand is coupled to global GDP development with an income elasticity.
4.Assumptions for electric vehicle stock calculations in the main text
The calculation of the number of battery-electric vehicles (BEV) in the different transport electrification sensitivity cases, as discussed in the main text, depends entirely on vehicle efficiency assumptions and the assumed distance that a typical BEV is driven each year. For this reason, we give ranges in the paper. The lower-end estimates assume the following: 20,000 km/vehicle/yr(12,420 miles/vehicle/yr) and 0.185 kWh/km (0.300 kWh/mile). The upper-end estimates assume: 10,000 km/vehicle/yr(6,200 miles/vehicle/yr) and 0.125 kWh/km (0.200 kWh/mile). We further assume that light-duty vehicles are responsible for 70% of the incremental transport electricity demand after 2010. Note that if some of these vehicles were plug-in hybrid-electrics (PHEV), the calculated number of vehicles would be far higher.