a global high shift scenario: impacts and potential for more public transport, walking, and cycling...

8/9/2019 A GLOBAL HIGH SHIFT SCENARIO: IMPACTS AND POTENTIAL FOR MORE PUBLIC TRANSPORT, WALKING, AND CYCLIN…

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L.M. Fulton, M. Replogle, R.M. Berliner

A GLOBAL HIGH SHIFT SCENARIO: IMPACTS AND POTENTIAL FOR MOREPUBLIC TRANSPORT, WALKING, AND CYCLING WITH LOWER CAR USE

Lewis M. Fulton

Co-Director, NextSTEPS ProgramUniversity of California, Davis1605 Tilia St., Suite 100Davis, CA 95616, USA

Phone: +1 (530) 752-3004e-mail: [email protected]

Michael A. ReplogleManaging Director for Policy and Founder

Institute for Transportation and Development Policy (ITDP)1210 18 th Street NW

Washington, D.C. 20036, USAPhone: +1 (212) 629-8001e-mail: [email protected]

Rosaria M. BerlinerInstitute of Transportation Studies

University of California, DavisOne Shields Avenue

Davis, CA 95616, USAPhone: +1 (347) 871-2742

e-mail: [email protected]

TRB Paper #15-1370TRB 94 th Annual Meeting, January 11-15, 2015


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L.M. Fulton, M. Replogle, R.M. Berliner

ABSTRACT

This paper summarizes new research evaluating a plausible “High Shift” (HS) scenario, in whichinvestment and policy around the world shifts toward favoring urban passenger travel by publictransport, non-motorized modes, and low-power electric vehicles rather than gasoline cars. Using

an urban model based on the International Energy Agency’s Mobility Model this scenario iscompared to a baseline projection in which rapid growth of private motor vehicle use continuesto 2050. In HS, global 2050 urban light-duty vehicle (LDV) travel is cut by half; in non-OECDcountries this travel is fully replaced by increased use of public transport and non-motorizedmodes, while in OECD countries person-kilometers of travel drop due to more compact land use,telecommuting, and changing lifestyles. This leads to near convergence of per capita urban

person-kilometers between OECD and non-OECD regions. Compared to the baseline, the HSscenario by 2050 would cut urban passenger transport CO 2 emissions by 43 percent, from 4.9 GTto 2.8 GT and trim cumulative 2010-2050 costs of vehicle purchase, fuel and operations andtransport infrastructure construction, operations and maintenance by US$113 trillion or 22

percent. The HS scenario would dramatically boost mobility for low and middle income people

worldwide, providing more equitable access to jobs, education, and healthcare, with lessautomobile-dependent systems that typically do not cater to lower income groups. Investmentrequirements and policy implications to achieve the scenario are considered, but more work isneeded to fully understand the challenges associated with achieving the envisioned future incities around the world.


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2L.M. Fulton, M. Replogle, R.M. Berliner

1. INTRODUCTIONUrban transport systems evolve in response to investments and policy choices made bygovernments about how to support different transportation modes. These can profoundly affectthe travel options and choices of individuals and businesses. Automobile dependency worldwide

is in part driven by underpricing and subsidies, planning, and investment practices (1) . The formsand modes of transportation used in cities can have a major impact on transport system performance. Yet different cities around the world have followed very different paths and patterns in terms of the manners in which people move, with some affluent OECD citiessustaining or growing very high dependence on public transport, walking, and cycling and others

pursuing car dependence. With rapid urbanization, further investigation of these relationshipsand choices is needed (2) .

While other studies have investigated these relationships and compared scenarios forspecific cities or countries, no other global/regional study that these authors are aware of hasattempted to estimate the global impacts of moving toward a world dominated by some types ofurban mobility systems rather than others. Making global estimates requires a fair amount ofsimplification of complex urban systems and variations from city to city, but it offers the

possibility of quantifying costs and benefits in a macro context. How much could CO 2 be cut?How much energy could be saved? What would it cost to develop urban mobility systems in adifferent direction than they appear to be headed today? How would system benefits and burdens

be distributed based on income?This paper reports on an 18-month research initiative undertaken by ITDP and UC Davis,

with funding from the Ford Foundation and ClimateWorks Foundation, to explore an alternativefuture and estimate its potential impacts, while considering what types of investments and

policies would be needed to bring such a future about. We consider two main future scenarios: a baseline urban scenario calibrated to the International Energy Agency’s (IEA) ETP2012 ’s 4° scenario (4DS) (3) and a newly developed alternative scenario called “High Shift”, with fargreater urban passenger travel by public transport and non-motorized modes than in the Baseline.

This project was inspired by the 2012 Rio+20 voluntary commitment by eightmultilateral development banks to devote $175 billion towards more sustainable transportinvestments over the next decade (4) as well as other voluntary commitments to double publictransport use and expand sustainable transport (5) . While this is only a small part of what it willtake to develop the needed transport systems, these inspire exploration of what a shift towardmore sustainable transport might look like, what it might cost, and what impacts it might have.

This analysis uses a somewhat simplified approach, though with considerable regionaland modal detail. It provides a base picture of urban travel around the world at a significantlyhigher resolution than any previous study – for example, with more modes and better estimatesof passenger travel by mode. The following sections describe the methodology, data andassumptions used in the study, the baseline and High Shift scenarios, and a range of results andimplications, with conclusions for policy making and proposed extensions of this research.

2. BACKGROUND

The analysis is developed using an urban model based on IEA’s Mobility Model (MoMo).MoMo allows a detailed representation to be made of travel at a national level, and for this

project this framework has been extended to focus on urban travel. MoMo contains some urbanmodes (e.g. city buses), and some modes are accounted for only at the national level (e.g. car


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travel). In this project, additional urban modes have been elaborated (e.g. metro, tram, commuterrail) and the urban share of all modes is estimated using the MoMo world framework of 32countries and regions (6) . The existing national projection system and scenarios form the basisfor our urban scenarios, including the baseline and alternative, “High Shift” scenario (HS).

Although there have been few macro studies of modal shift potential, there are important

pre-cursers to this one. The 2009 Moving Cooler study (7) evaluated four dozen transportstrategies and policies that would affect United States motor vehicle activity and use, bundled invarious ways under different scenarios. It analyzed their impact on overall U.S. CO 2 emissionsout to 2050 considering baseline and forecast travel markets using a motor vehicle stock model . This formed the foundation of a related report to Congress (8).

More recently, and directly related to this project, the IEA used MoMo to generate projections of total world transport and energy use across three scenarios, called 6DS, 4DS and2DS, related to three atmospheric temperatures the scenarios result in (3) . In particular, the ETP2012 included an “Avoid/Shift” scenario that focused on shifting travel patterns, while holdingother things (like vehicle efficiency and fuel types) the same as the Baseline (4 °) scenario. This

provides a foundation for the HS scenario presented here. The IEA Avoid/Shift scenario included

all modes, not just urban, and for passenger travel targeted a 25% reduction in car as well as inair travel in 2050 relative to the Baseline, with much of this travel shifted to bus and rail (andwith somewhat lower travel growth overall). IEA found this scenario cut energy use and CO2emissions by 20% compared to the baseline, with lower costs for vehicles, fuels andinfrastructure. This study extends the IEA work by providing more detailed urban analysis, moreambitious modal shifts corresponding to larger investment in public transport, walking, andcycling, and deeper analysis of potential impacts.

3. APPROACH, METHODOLOGY AND DATA ISSUES

MoMo is a straight-forward accounting system and the strength of the model is its detail – itcontains a consistent representation of populations, travel per capita, travel mode shares, vehiclesales and stock, vehicle efficiency, and the extent of road and rail infrastructure used by urbanvehicles. Using travel/energy identities, it estimates the resulting energy use, emissions, andcosts that are associated with the estimated and projected travel. MoMo projects all variables to2050 using 5 year increments, with UN urban population projections and average nationalGDP/capita as important underlying drivers of travel growth.

Traditionally MoMo has been a vehicle/technology oriented model, but in recent yearsthe travel structure of the model has been improved. For this project this travel and mode-choicerepresentation has been translated into the urban context, primarily by adding detail on urbanmodes such as metros, trams, BRT, and commuter rail, and by estimating the urban share ofindividual travel by car and 2-wheelers and relating these to UN urban population forecasts, withnew detail on cost, trip making and demographics. Global databases have been developed onurban rail systems (metro, tram/LRT and commuter rail); for bus systems, the ITDP BRTdatabase has been used. Estimates of urban travel by car, 2-wheelers (motorcycle/scooter),walking, and cycling have been made, though there is little data on the use of these differentmodes per capita for most cities around the world, so many assumptions have been made. Amore complete documentation of assumptions and data used in this analysis will be madeavailable on the websites of ITDP and UC Davis by the time of the TRB 2015 conference ( 9).


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This new “Urban MoMo” has been developed and used mainly in a back-casting format,to provide a plausible pathway to achieve a specific 2050 target in each country and regioncovered. Deviations from the Baseline are developed to gradually move toward the alternativetargets, creating a single alternative future; this future is then analyzed in terms of itscharacteristics and impacts. Many other alternative futures could be imagined that are not

considered here.

3.1 BASELINE PROJECTIONIEA ETP 2012 MoMo 4°C global warming scenario (4DS) provides the basis for this study’sBaseline scenario ( 4). While IEA’s 6°scenario appears to be closer to the current path the worldis on, there are reasons to believe that a 4° future is more likely at this point, given recent policyactivity ( 4). 4DS assumes—among other things—a global climate agreement that creates aglobal CO2 pricing system to restrain GHG emissions growth, but without sector-focused shiftsin investments and policies that might flow from concerted pursuit of broader sustainabledevelopment goals ( 4).

This baseline builds on recent trends in travel around the world, including a continued

strong rise in car ownership and use as incomes rise, with rapid increases in air travel as well. Inthe urban context, car and (in some regions) motorcycle travel mode shares rise rapidly, withtravel by mass transit, walking and cycling slow growing or stagnant in most regions. Fuelefficiency improvements occur fairly slowly except where fuel economy standards are in place;alternative fuels do not gain much traction and petroleum fuels still dominate in 2050. Overallwe project the baseline urban transport share of total world transport energy use to rise fromapproximately 28% (35% of land transport) in 2010 to 33% (42% of land transport), whencomparing our projections to those of the IEA 4DS.

3.2 THE HIGH SHIFT SCENARIOThe HS scenario has been built up assuming major departures from the Baseline in terms oftravel trends, particularly after 2020. The same overall growth trajectories in travel are assumed

but shifts to transit and non-motorized modes gradually occur (or shifts away from these modesare greatly slowed) based on much better provision of high quality options in cities worldwide.This in turn requires major investments in new systems and provision of infrastructure such asBRT, rail, bike lanes, that are estimated in connection with the scenario. Targets for urban andmetropolitan area transit system development and associated passenger travel are linked to theUN 2014 revisions of urban population through 2050 (with explicit projections for individualcities to 2030), with urban population rising to 66% from 50% today (10).

A primary objective in developing the High Shift scenario is to consider what couldhappen if policies and investments in parts of today’s world enjoying more efficiently managedurban transport were to be extended to most parts of the world. In other words, to take-to-scale

best practices that expand travel on high efficiency modes and low-carbon modes as much asseems plausible while reducing travel (growth) from private cars/SUVs as much as plausible inan off-setting fashion. Assumptions in developing the HS include:

• Total urban passenger mobility through 2050 (measured as passenger-kilometers) isroughly preserved from the Base scenario in the same year and region. However in somecases lower levels of travel are accepted as part of improved urban planning that lowerstrip lengths, particularly in OECD countries.


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• For private motorized modes, the ownership rates projected in the baseline that arerelated to income growth are over-ridden by assuming lower rates, along with lowertravel per vehicle and somewhat higher occupancy rates. All of these would need to beachieved through policy and pricing initiatives, since autonomous changes in lifestylethat might affect car ownership are already included in the baseline.

•

For transit modes, the average number and length of systems, as well as the modalcapacity, frequency, speeds and load factors are all increased in HS in order to generatehigher passenger-kilometers (pkm) estimates. These are all checked against data onexisting high-performing systems, with the idea that the future average system would

perform closer to today’s best systems.• It should be noted that neither travel speed nor travel time have been considered. We

assume that land use develops appropriately for the types of modes we are shifting to andthat trip lengths are shorter through increased network efficiency, transit orienteddevelopment, and the implementation of other traffic demand management tools. It isimplicit in the HS scenario that public transport vehicles, walking, and cycling are givenincreasing priority in street space allocation to ensure some growth in the productivity

and attractiveness of these modes in the traffic system, while car traffic in cities ismanaged effectively with parking policies, pricing, and sound traffic operationsstrategies.

3.3 URBAN RAPID TRANSIT AND CITY SIZE PROJECTIONSA key aspect of the projections in HS is growth in urban rapid transit systems, particularly rapidtransit such as metro, tram/light-rail (LRT), commuter rail and bus rapid transit (BRT) systems.To project the extent of these systems, we estimated their extent in cities around the world today,and developed targets for their expansion and new construction in cities out to 2050. A city sizeanalysis was undertaken in conjunction with data on system location and extent, to identify

patterns. We extended from 2030 to 2050 the UN projection of cities by city size based on the

UN projection of total urban population to 2050.

Using the projection of cities of different sizes, several observational approaches were used toidentify target levels of rapid transit system extent for different size cities. After a detailed globaldatabase of existing systems was developed, these were sorted by city size and region around theworld. We considered the largest systems per capita by city size by region and the average ratiosof system length to population. A wide range of maxima occur with no particular pattern; citiesin Organization for Economic Cooperation and Development (OECD) regions generally havelarger systems per capita than in non-OECD. Europe has particularly large systems, as Table 1shows. It also has much higher percentages of cities with systems than do most other regions.


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TABLE 1 Rapid Transit to Resident (RTR) Ratio 2014 and High Shift Scenario: Km permillion residents by mode and region (averaged over all cities over 300,000 population)

Metro2014

BRT2014

Tram/LRT2014

Commuterrail 2014

2014Total

2050HighShift

Scenario

TotalUSA and Canada 5.0 0.4 4.8 19.7 30.0 62.3OECD Europe 7.5 0.6 14.3 54.1 76.5 134.7OECD Pacific-Other 8.3 0.9 2.8 61.6 73.7 127.3

Non-OECD Europe 4.9 0.0 26.1 1.8 32.7 70.9Russia 4.6 0.0 23.2 0.0 27.8 72.4China 3.0 0.7 0.3 0.1 4.1 51.3India 0.6 0.2 0.2 3.6 4.6 45.6Other Asia 0.8 0.5 1.0 0.6 2.8 35.0

Middle East 1.8 0.7 0.2 0.1 2.9 37.7Africa 0.2 0.2 0.4 1.6 2.4 29.8Other LAC 0.8 1.9 0.2 7.8 10.7 34.0 Mexico 2.1 1.9 2.8 0.3 7.2 34.5Brazil 1.8 1.6 0.0 4.1 7.6 33.9

Statistical analysis was undertaken to evaluate relationships between system length and citycharacteristics but found no significant correlations between city density or city GDP per capitaand system size. The only consistently significant variable was the population of the city.

Ultimately a fairly simple approach was used to choose system targets for average citiesin each part of the world that are also shown in Table 1. The propensity of European cities to

build extensive systems and the greater resources available to most OECD regions and Chinawere noted, with targets set by region. The difference in the target size of systems per cityreflects both differences in average size but also to some extent differences in the numbers ofcities with systems. The objective in this scenario is for as many cities as possible to have arange of rapid transit systems, but realistically not all cities in a region will likely participateregardless of the “global push” for transit that is implied in this scenario.

3.4 ADDITIONAL ASSUMPTIONS: URBAN BUSES In the HS scenario, apart from “rapid transit” buses (BRT systems), there is steady growth in thenumber of buses around the world, particularly in non-OECD countries. Assumptions include:

• Ridership per bus increases from a 2010 range of 6-47 (US and Eastern Europe,respectively) to a range of 20-50 in 2050, with most countries in the world in the 25-30range by 2050. In contrast, in the baseline scenario, load factors generally decline.

• Minibuses (under 24 seats), use MoMo 2010 base year data, with slow worldwide baseline growth in numbers and ridership vs. a decline in the HS scenario, as riders shiftto larger buses and BRT.

• In HS by 2050, most cities have sizeable BRT systems, particularly in the developingworld. Apart from the projection of BRT system growth, BRT ridership per unit systemlength approaches the TransMilenio system in Bogota, with similar bus capacities, loadfactors and vehicle speeds. All systems achieve at least a bronze or better ITDP BRTStandard (10), by 2050 yielding 30-35 million pkm per lane-km for BRT (compared to


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40-42 million pkm per lane-km for Metro, up from 12-14 and 25-35 million pkm perlane-km respectively today).

• BRT is assumed to pull riders from motorized 2-wheelers, light-duty vehicles (privatecars), minibuses, and regular buses.

3.5 ADDITIONAL ASSUMPTIONS: URBAN RAILA major effort was made to build up a worldwide inventory of rail systems and systemcharacteristics (system length, ridership). The International Union of Railways (UIC) providedIEA with an initial database of tram, light Rail, and metro systems, which was augmented withinternet searches and national, regional, and local government, and transit operator data. Acompletely new commuter rail database was constructed. Assumptions include:

• In 2010, by far the highest urban rail ridership is in Europe and OECD Pacific. Manyworld regions have comparatively few systems and lower ridership levels on thosesystems.

• In the Baseline scenario, rail systems do not expand much and not that many new systemsare built, so there is only slow growth in urban rail ridership around the world.

•

In HS, there is steady growth in the number of rail systems and ridership around theworld to reach certain targets of rail access and ridership, though the levels per capita inmany regions in 2050 are still well below Europe and OECD Pacific today.

• Metro, trams and light rail are featured more in OECD countries whereas BRT is featuredmore in non-OECD countries, though all regions grow all systems to some extent.Commuter rail systems are expanded significantly in all regions as part of a poly-centricdevelopment strategy for metropolitan areas.

3.6 ADDITIONAL ASSUMPTIONS: LOW-POWER AND NON-MOTORIZED MODESWalking is poorly evaluated world-wide due to lack of common definitions and analysisframeworks. Virtually everyone walks daily to help meet their basic needs for some combination

of access to food, water, community, work, education, health care, shopping, and recreation.Some of these walk trips are access to public transportation, or to cars parked near a trip end.Including all short trips, there may be as many as several walk trips a day per person worldwide,making walking the dominant travel mode by trip share. This study, like many, excludes manyshorter trips on foot, relying on 2010 data mainly from urban travel surveys which rarely includean explicit accounting of all foot travel linked to other trips, or even the distances covered in fullwalking trips. Somewhat more walking trips are assumed in non-OECD than OECD countries,with the most trips per capita in Africa. Baseline walking is assumed to be relatively unchangedcompared to 2010, though with a slight decline in distance per capita; trip share is increased inHS compared to 2010 to reflect the greater possibility for safe, convenient urban walking tripswith proper infrastructure and more compact land-use planning.

The HS scenario assumes an increase in the use of low power e-bikes and bicycles incountries that don’t already have high levels of use. While in the reference case there are highlevels of walking in most countries and high levels of biking in a few countries such as the

Netherlands, in HS the walking and biking trips would increase among people with motorizedoptions such as access to cars. Electric bicycles and low-powered electric scooters (collectivelycalled “e-bikes”) are in widespread use now only in China, but in HS would increase worldwide.These are distinguished from higher powered, fast scooters and motorcycles and, if regulatedappropriately, could contribute to slower traffic speeds and safer conditions in areas where they


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become prevalent. We hypothesize future ownership levels that appear possible, and average use per day and per year to generate PMT projections. Assumptions include:

• Regular bike ownership is explicitly estimated and modeled and follows use patterns thatappear consistent with existing literature ( 11 ). Fairly good data exists on bicycle stocksaround the world, but average daily use of bicycles is poorly documented. We assume

relatively low daily use factors.• Bike use will rise as investments are made into bike lanes and parking, safety features,

and supportive policies, as has happened in various cities ( 12) and as projected by othermodeling ( 13). Here it has been assumed that most cities could achieve somethingapproaching average European cycling levels by 2050 but have only a fraction of levelsachieved today in Amsterdam or Copenhagen. Much higher shifts for cycling would be

plausible with more supportive infrastructure and policy.• For e-bikes, it is assumed that ownership is currently near zero except in China and parts

of Southeast Asia. Growth in ownership and use is based on slowly rising rates, and acomplementarity of use between e-bikes and bicycles. In addition, the use of internalcombustion engine (ICE) scooters and motorcycles in the HS scenario is set to decline

with much replacement by e-bikes over the coming 35 years. As a result, total travel viae-bikes + ICE 2 wheelers does not grow much on net.• The total NMT pkms rise for all three modes over time, but more dramatically for e-bikes

with much slower increases for walking and biking.

4. RESULTS

To achieve the HS projection of urban passenger travel, the increase in travel by each mode wascombined (with consideration of how much each of these modes could logically increase givenincreases to the others) and then compared to total travel in the Baseline, for each of the regionsand countries in MoMo. Growth rates in non-OECD countries were adjusted to support a target

50% reduction in private light-duty vehicle travel, except in the OECD countries, where totalurban passenger travel is supported by more intensive travel demand management through urban planning that shortens trips, telematics and pricing, and demographic shifts. Some increase in theaverage occupancy of light-duty vehicles occurs (e.g. via more ridesharing), so the pkm share ofcar travel does not drop by quite as much as vehicle kilometers do. These summary results forOECD and non-OECD regions of the world are shown in Figure 1a for total pkms and Figure 1bfor pkm per capita. This reveals that in 2010 those in the OECD travelled almost twice as much

per person as in the non-OECD, while by 2050 in the HS scenario, the travel per capitaconverges around 8,000 kms per person per year, suggesting more equal levels of mobility thanexist today or in the baseline scenario. Results in greater regional detail along with detailedassumptions and calculations are being prepared in a documentation report that will be made

available from the authors ( 9).


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FIGURE 1 Urban travel results of High Shift Scenario v. Base scenario, OECD and non-OECD, in 2050: total passenger-kilometers (1a) and per capita (1b).


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4.1 SCENARIO IMPACTS: ENERGY AND CO2 EMISSIONSSince all urban areas in the world are included in the analysis, energy use and CO2 emissionsimpacts can be reported at a global and regional level. Energy use is a function of the vehicletravel and vehicle efficiency for each mode, calculated taking into account load factors and thenumber of vehicles and vehicle kilometers needed to move people the specified passenger-kms.

Energy efficiency of different types of vehicles (based on MoMo vehicle efficiency estimates,adjusted for urban in-use conditions) varies greatly, but not that much regionally. It does improvesignificantly over time in the Baseline scenario, with identical improvements under HS.

Apart from the levels of travel, the critical assumptions behind the energy use and CO2numbers are the efficiency of the vehicles and the ridership on those vehicles. For each regionand mode, Figure 2a shows efficiency per passenger kilometer and Figure 2b shows total energyuse. Public transit modes are far more efficient than light-duty vehicles, so shifts to these modescuts energy and CO2 per passenger-km significantly. Efficiency per passenger-km improvesmore in the HS because ridership per vehicle is higher than in the baseline, based on assumedimprovements in system management, high quality services, urban densification, etc.

FIGURE 2 Energy Efficiency (2a) and Energy Use (2b) by Scenario, Region, and Mode

The resulting CO2 emissions by mode is shown in Figure 3. The dominance of light-dutyvehicles in current and baseline future energy use and CO2 emissions is evident, as is thereduction in energy and CO2 emissions in the HS scenario. Compared to the baseline, the HSscenario by 2050 would cut global urban passenger land transport CO2 emissions by 43 percent,


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from 4.9 GT to 2.8 GT. Specific fuel types are not shown but road modes are dominated by petroleum fuel while rail modes are almost entirely electrified, as are e-bikes. Electricitygeneration is decarbonized over time in line with the IEA 4° scenario. This is helpful but notcritical for experiencing substantial reductions in CO2 from the High Shift scenario.

FIGURE 3 Urban Passenger Land Transport CO2 Emissions by Mode, Region andScenario.

4.2 TRANSIT SYSTEM INFRASTRUCTURE REQUIREMENTS As described above, the system size (and thus infrastructure length) needed to support BRT and

urban rail travel was estimated using assumptions of the number and lane-kms of systems in place around the world. These projections were in turn used to develop the infrastructure costestimates associated with these scenarios presented below. The total kilometers of system length

by region and year for the High Shift scenario is shown in Figure 4. In the OECD, the increasefor each mode is significant compared to 2010 but not huge in percentage terms (except for BRTwhich is tiny in 2010). In non-OECD countries the required growth rates are far higher andwould require major, sustained investments over the coming decades to achieve. Growth isfastest for BRT and commuter rail.


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FIGURE 4 2010 & High Shift Rapid Transport System Length by Mode by Region.

4.3 COST IMPLICATIONS OF THE HIGH SHIFT SCENARIO The major direct cost and investment implications of the High Shift scenario have beenestimated, relative to the baseline, from 2010-2050 in a cumulative and annual average fashionincluding all market costs to private users and public agencies (i.e. taxpayers):

• Vehicle purchase costs for all types of vehicles, all modes• Fuel costs for all modes and vehicle types• Vehicle and transit system operating and maintenance costs, including daily O&M costs

and infrastructure maintenance costs.• Infrastructure capital costs, i.e. the one-time investment costs to construct roads,

sidewalks, parking lots and structures, BRT systems, rail and bus systemsThese estimates are based on averages from various reports, by country or region ( 4).

The cost analysis is summarized in Figure 5. Costs rise as a function of passenger travelgrowth by mode and region. So, for example, the cost of infrastructure for roads and transitsystems rise in proportion to their importance in the two scenarios. Road and parking costs arefar lower under HS than in Baseline. Transit system construction and operation costs are farhigher under HS than Baseline. HS has far lower energy requirements and so creates largeenergy cost savings.

Overall the total costs of the Baseline between 2010 and 2050 are roughly $500 trillion($200T in OECD and $300T in non-OECD), whereas the costs in the HS scenario are about $400trillion ($160T in OECD and $240T in non-OECD). The HS scenario would trim cumulativecosts by US$113 trillion or 22 percent.


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FIGURE 5 Summary of cost estimates 2010-2050 by scenario and region.

Figure 5 represents the monetary investment costs for the baseline and High Shiftscenarios. We have not directly investigated or attempted to value consumer preferences;however, by preserving most PKT, we are attempting to preserve (and in some cases enhancemobility and access. More specifically, the levels of use of various modes per unit of service

provided are based on current consumer preferences and behaviors with some shift towards better practice standards of service. Consumer preferences are shaped by the overall travel choicesets available to consumers, with their varying attributes of convenience, time, cost, and comfort.This study implicitly considers the variability in consumer preferences across the spectrum ofcurrent income and car ownership levels observed globally and how infrastructure investmentand policy can shift those preferences towards lower or higher mobility by light duty vehicles.

4.4 EQUITY IMPLICATIONS OF THE HIGH SHIFT SCENARIO In addition to developing an urban version of MoMo, a new demographic breakout of urbantravel was developed and linked to this urban projection system. This first generation“Demographic Equity Economics” model provides the opportunity to track travel by groupswithin the population. The data foundation for this was a review of 25 national and urbanhousehold travel surveys from around the world. This showed that few of the databases (orassociated analyses) were directly comparable, using different methodologies, differentquestions, different group definitions and different mode classifications for travel. However, dataon car ownership by income category was found to be sufficiently comparable to establishapproximate base year travel mode shares for a number of regions (e.g. 14-17 ).

For 2010, passenger travel by mode across income groups sums to total travel on thatmode from the broader study; the main uncertainty is how the ridership breaks out across incomegroup going forward in time. Total travel is assumed to be significantly lower for lower incomegroups, as suggested in travel surveys, but this difference declines somewhat as the poorestquintiles’ income grows. Projections were constructed for 14 regional breakouts by incomequintile. Another important cross-check for this projection is that car ownership is a function of


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the income of each quintile, based on a global income-ownership study ( 18). Current Incomedistributions are taken from World Bank data ( 19), total income projected in line with OECD

projections used in ETP 2012 ; income breakouts are assumed to retain the same distributional patterns over time (no changes in GINI coefficient).

Despite uncertainties, the breakout of travel into income groups provides important

insights. Compared to 2010, baseline passenger-kms in 2050 about doubles. Much of this is fromincreases in car ownership among higher income groups. Under the baseline, as in today’s cities,higher automobility by upper income travelers can be expected to result in higher trafficcongestion and competition for street space, which degrades the quality of public transport,walking, and cycling that are used by lower income groups. Under HS, there is much moregrowth of transit and NMT, rather than car growth. As availability of transit and NMT facilitiesexpands and ridership expands, more street space ends up being allocated to lower incomegroups than for the cars used by mostly by the affluent. Thus, the bottom two quintile groups

benefit disproportionately from transit/NMT improvements, as do the top two quintile groupsfrom increases in car travel infrastructure growth.

In 2010 and even in the 2050 baseline, lower income groups have relatively low mobility

and very low car access, as Figure 6 shows. The vast majority of humanity is unlikely to haveaccess to a car even in 2050. In the HS scenario, there is much more even mobility acrossgroups. Figure 7 reflects this rebalancing of travel by mode with a smaller difference in travel

per capita in 2050 between the lowest and highest income groups under HS compared to theBaseline.

FIGURE 6 Car Stock By Income Group 2010 vs. IEA 2050 4° vs. High Shift Scenario.


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FIGURE 7 Travel per capita by mode, income group, region and scenario.

5. CONCLUSIONS Given the assumptions made and scenarios compared, the main finding is that a high-transit,high-non-motorized-vehicle scenario that (at least in the developing world) provides similar totalmobility (in passenger kilometers) as a baseline, more car-dominated scenario, is likely to bemore equitable, less expensive to construct and operate over the next 40 years, and to sharplyreduce CO2 emissions.

This scenario is one example of many possible futures. It is not a prediction and may beextremely challenging to achieve, requiring high rates of public investment. Strong policies and,more specifically, a major re-direction in investments would be required to achieve the outcomesoutlined from this High Shift scenario. A principal purpose is to use this scenario as the basis to

investigate the implications of this future for a range of impacts and indicators of interest. Is highquality mobility and access preserved? What might be the environmental, safety and healthimpacts? What might the impacts of this future be for public finance, job creation and economicwell-being and overall sustainable development? These aspects are being further investigated.

REFERENCES1. Jean-Paul Rodrigue, Claude Comtois and Brian Slack, The Geography of Transportation

Systems, Routledge, New York, 2006.

2. UN HABITAT, Planning and Design for Sustainable Urban Mobility: Global Report on Human Settlements , Nairobi, 2013.

3. International Energy Agency, Energy Technology Perspectives 2012, Paris, 2012.

4. Commitment to Sustainable Transport: Joint Statement to the Rio+20 United NationsConference on Sustainable Development by the African Development Bank, Asian

Development Bank, CAF, Development Bank of Latin America, European Bank for Reconstruction and Development, European Investment Bank, InterAmerican Development Bank, Islamic Development Bank, and World Bank , June 2012.


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5. Partnership on Sustainable Low Carbon Transport, Creating Universal Access to Safe,Clean and Affordable Transport , Shanghai, 2013.

6. International Energy Agency, The IEA Mobility Model As of February 2014 , Paris,http://www.iea.org/media/transport/IEA_MoMo_Presentation.pdf

7. Cambridge Systematics, Inc. Moving Cooler: An Analysis of Transportation Strategies for Reducing Greenhouse Gas Emissions. Washington, DC: Urban Land Institute, 2009.

8. U.S. Department of Transportation. Transportation’s Role in Reducing U.S. GreenhouseGas Emissions, Volume 1: Synthesis, Report to Congress. April 2010.

9. Fulton, L., Berliner, R., and Gettani, D., Forthcoming, “Analysis of Modal Shift Potentialand Impacts, for Urban Passenger Transport World-Wide”.

10. Institute for Transportation and Development Policy, The 2014 BRT Standard , New

York, 2014.11. UN DESA, World Urbanization Prospects, New York, July 2014 .

12. Buehler, R. and J. Pucher. ‘Walking and cycling in Western Europe and the UnitedStates: Trends, policies, and lessons’, TR News 280 , May-June 2012, p. 34–42

13. MacMillan, Alexandra, Jennie Connor, Karen Witten, Robin Kearns, David Rees, andAlaistair Woodward, “The Societal Costs and Benefits of Commuter Bicycling:Simulating the Effects of Specific Policies Using System Dynamics Modeling,”

Environmental Health Perspectives, Vol 122, No.4, April 2014.

14. Secretaria Distrital de Movilidad. Informe de indicadores: Encuesta de Movilidad deBogota 2011, Link :http://www.movilidadbogota.gov.co/hiwebx_archivos/audio_y_video/Encuesta%20de%20Movilidad.pdf. Accessed March 12, 2014.

15. The World Bank. A Gender Assessment of Mumbai’s Public Transport. Mumbai, India.June, 2011. Link: https://openknowledge.worldbank.org/handle/10986/12347. AccessedApril 27, 2014.

16. Development Bank of Latin America. Observatorio de movilidad urbana (Urban MobilityObservatory), 2007. Link: http://omu.caf.com/media/15966/omu_movilidad.xls .Accessed March 15, 2014.

17. Hanoi, Vietnam: The Comprehensive Urban Development Programme In Hanoi CapitolCity of The Socialist Republic of Vietnam (2007) Link:http://dc596.4shared.com/doc/Vwk5nDqL/preview.html


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18. Dargay, J., D. Gately and M. Sommer, 2007, “Vehicle Ownership and Income Growth,Worldwide: 1960-2030”, January 2007, Link:http://www.econ.nyu.edu/dept/courses/gately/DGS_Vehicle%20Ownership_2007.pdf

19. World Bank Poverty and Inequality Database, 2014. Link:

http://databank.worldbank.org/data/views/variableselection/selectvariables.aspx?source= poverty-and-inequality-database#

a global high shift scenario: impacts and potential for more public transport, walking, and cycling...

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