Assessing how the Clean Development Mechanism can Increase Bicycle Use in Santiago

CCAP, IISD, CC&D August 25, 2004

1

Assessing how the Clean Development Mechanism

can Increase Bicycle Use in Santiago

Steve Winkelman & Erin Silsbe

Santiago, Chile

August 25, 2004


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Overview

• Introduction and context• Bicycles and the CDM• Methodological Issues• Sample Calculations• Initial Conclusions• Respondents


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Bicycle Use Offers ManySocietal Benefits

• Improved air quality• Lower energy use and GHG emissions • Reduction of traffic congestion • Promotion of healthier lifestyles • Traffic safety• Social equity, poverty reduction


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Cycling + Walking = Lower Emissions

y = 2508.8x-1.2786

R2 = 0.8647

0

10

20

30

40

50

60

0 20 40 60 80 100

Per Capita Transport Energy Use (GJ)

NM

T M

od

e S

ha

re (

% o

f all

Tri

ps

)

USEngland

Canada

Netherlands

FranceGermany

Italy

Non-Motorized Mode Share and Annual per Capita Energy Use

Source: IPCC, 1995, Pucher et. al


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Context: Santiago Mode Split

Note: 2001 O-D data adjusted for comparison with 1991

0%5%

10%15%20%25%30%35%40%45%50%

Bus CarTax

i

Met

ro

Wal

king

Bikes

Other

Per

cen

t o

f T

rip

s

1991

2001


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Santiago: Mode Share by Distance

Fuente: LABTUS para IISD, 2004

Mode share by distance (first 5.000 mts.) and mode

0%

20%

40%

60%

80%

100%

120%

CPr

NMT

SPr

CPu

NPu

SPu

Short trips are disproportionately polluting…these are the trips that are most suitable for non-motorized transport (NMT)


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Potential Bicycle Projects & Policies• Bicycle projects could include

– bike lanes– segregated bikeways– parking facilities– promotional activities– incentives– bicycle signage – traffic signal improvements

• Comprehensive package– The measures above plus

extensive connectivity in the bicycle network


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International Comparison

Amsterdam, Netherlands

Bogota, Colombia

Santiago, Chile

Population (2003) 736,000 6,981,000 5,333,100 Area (km2) 210 1,587 2,000 Avg. Population Density 3500 4400 2600 Bicycle Paths (km) 400 300 Bikeway Density 1.9 0.2 % Bike Trips 23% 2% 1.9%


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Bicycle Potential in Santiago

Ortúzar et al. (1999):• Bicycle use in Santiago could theoretically increase

to 5.8% of all trips with implementation of a major network of bikeways (3.2 km of bikeway per km2)

…If even a small percentage of trips were diverted from the private car, the reduction of fossil fuel consumption, greenhouse gases and air pollution could be significant


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Our Project: Bikes and the CDMPurpose• Assess how the CDM can be used to help increase bicycle

use to reduce motor vehicle emissions in Santiago

Approach• Address methodological issues• Consider two different scales

– An individual bikeway project– A Comprehensive “Santiago-wide” policy


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Methodological Issues• Forecasting Bicycle Use

– How many additional bike trips of what length are expected from the project?

• Baseline– How would travel have occurred in the absence of the

proposed project activity (car, bus, etc.)?– Must take into account existing projects, policies (e.g.,

Alameda, GEF bikeways)

• Monitoring– Determine number and length of new trips

• Avoided emissions– Difference between actual emissions and those that

would have occurred had new trips followed the baseline mode split


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Forecasting Bicycle Demand (1)

Rough Estimates• Typical: either no forecasting, or simplistic assumptions• Comparison studies (before-and-after, similar conditions)• Aggregate behavior (e.g., regression on population characteristics)• Rules of thumb, multipliers, adjustment factors (e.g., CARB)

Measures of Potential Demand• “Revealed” preference surveys (e.g., from traffic counts)• “Stated” preference surveys (attitudinal or hypothetical)

Note: This section based in large part upon the U.S. Federal Highway Administration (FHWA) NMT Guidebook


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Forecasting Bicycle Demand (2)Discrete Choice Models (e.g., logit model)• Widely used to predict mode choice• Based on “stated” or “revealed” preferences• May require extensive survey data and technical expertise• Very useful for isolating effects of specific factors

Regional Travel Models• Most models ignore pedestrians & bicycles

– Traditional modeling techniques ineffective for bicycles (Katz)

• Rough adjustments are typical (e.g., pedestrian environment factors)

• Requires significant data and technical expertise• Can be powerful tool but significant research needs remain


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Forecasting Bicycle Demand (3)

Ortuzar, Iacobelli and Valeze (1999):

“Estimating Demand for a Cycle-way Network”• Household survey (stratified sample)• Stated preference mode choice survey• Logit model on willingness to cycle• Generated trip matrices to plug into the regional travel

model, ESTRAUS– Assumed a bikeway network of 3.2 km per km2

• Calculated that bicycle use in Santiago could increase from 1.6% to 5.8% of total trips


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Baseline Data NeedsIdeal data

• Projected mode split for short trips along the affected corridor or in that specific neighborhood?

Acceptable data• Current mode split for all short trips in the region

Minimum Necessary data• Current mode split for all trips for the region


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Available Baseline Data in SantiagoIdeal?• ESTRAUS forecast for short trips

Acceptable?• 2001 O-D data on mode split for short trips• Simplistic forecast based on extrapolation of trends

(e.g. 1991-2001)

Minimum Necessary?• 2001 O-D data on mode split for all trips


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Dynamic Baseline• Use actual (not projected) mode split data

– For all short trips or – For short trips in places with similar land use characteristics

and demographics

• Account for factors that influence bicycle use– Motor vehicle characteristics

• Car ownership• traffic in surrounding area

– Demographics• Population• Age distribution (e.g., number of students)

– Economic variables• Fuel prices• Gross National Product

– Other projects and policies

• Attractive in theory, but complicated in practice?


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Key Baseline Challenge

• Can the baseline be defined sufficiently well that bike count data can be used to assess the travel and emissions impact?– Is it necessary to determine who are new riders?– Would surveys asking cyclists what travel mode they

would have used without the project increase certainty?


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Additionality

• Bicycle projects seen as additional because:– No regulation requires development of bikeways– There is limited investment in bikeways in Santiago

(e.g., need GEF investment)– Cultural and image (pscyhological?) barriers appear

to prevent greater bicycle use


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Forecasting Travel Impacts• Shorter term, many bikeway users may be

lower income and shifting from bus• Longer term, with comprehensive network

more people might shift from cars to bike– This longer term effect is inherently reflected in the

2015 mode split forecast assumptions


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Monitoring (1)

Bicycle Counts• Survey points: Natural barriers or define “screen” lines• Frequency and Duration: short counts more useful than

infrequent all-day counts to reflect change over time• Periods: Peak, off-peak, lunchtime

– May differ from motorized modes

• Note weather conditions, singular events• Use of automated counters is worth exploring

– Tampering concerns?

Based upon Hudson, Bicycle Planning: Policy and Practice (1982).


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Monitoring (2)

Surveys• Roadside, destination-based, home-based• Establish: trip length, purpose, route, alternative mode or

route choice (without project)

Balancing Robustness with Practicality• What frequency and scope are sufficient?

– Statistically significant?

• Comprehensive policies can be tracked with regional vehicle-km traveled and mode split data

• Isolating the impacts of specific small-scale projects may be overly resource intensive (GEF $30,000 for basic survey work)

• Update dynamic baseline with demographic & traffic data


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Assumptions for Sample Calculations: Emission Factors

Car: 141 g CO2 per passenger-km• Assume loading of 2 people per car

– Reflects that reduction of car passengers does not necessarily imply a reduction in number of car trips

Bus: 40 g CO2 per passenger-km• Assume loading of 40 people per bus

– While high for a daily average, this is intended as a conservative assumption. One could also argue that no emissions are displaced with a bus-to-bike shift.

Other: (walk, bike, metro, taxi) Assume no displaced emissions (conservative)


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Short-Trip Mode Split Assumptions for Sample Calculations

2001 Mode Split (< 3km) Assumed 2015 Mode Split(2001 O-D survey, no. of trips) (business as usual)Bikes 2.5% 2.5% 0.0%

Bus 9.1% 6.0% -3.1%

Car 17.5% 27.0% 9.5%

Walking 62.2% 56.3% -5.9%

Metro 1.9% 1.7% -0.2%

Taxi (inc.collectivos) 6.4% 6.0% -0.4%

Other 0.5% 0.5% 0.0%

100% 100.0%

Short-trip mode split data from DICTUC

Note: 2015 based on extrapolation of 1991 -2001 growth trends (for all trips)


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Cost AssumptionsInfrastructure Costs• Range: $70 - $100K+ per km of bikeway (GEF, SECTRA)

• Other determining factors– lighting, maintenance, signs, intersection modifications, traffic signaling,

enforcement, cost sharing arrangements, etc.

• Bike Lanes cost only 5% of segregated bikeways (SECTRA)

CDM-Related Costs and Benefits• Emission credit value: We assume $5/tonne for calculations• Monitoring costs?• CDM project cycle costs?• Cheaper if small scale projects are bundled? • Co-benefits not included


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Project Example: New Bikeway

Assumptions• 4.5 km bikeway• Baseline: Estimated 2015 future mode split (above)• Average round-trip length: 6 km

Emissions Savings• With 1,000 users/day, 260 days/year:

63 tonnes CO2 per year

Costs• $80,000 per km• Over 10 years: $533/tonne CO2 • At $5/tonne CERs only contribute 1% of total costs

– Enough to help with maintenance costs?


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Policy Example: Comprehensive Bicycle Network

Assumptions• Assume total trips double from 2001 – 2015

– based on 1991 -2001 growth rate

• Use estimated future mode split for short trips: • Average round trip length: 6 km• 260 weekdays per year• 1,200 km bicycle network

– 600 km bikeway– 600 km bike lanes

• $58,000 per km (average from CONASET)


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Policy Scenarios: Annual Savings and Costs in 2015• Increase bike mode share from 1.9% to:

3% (conservative), 6% (Ortúzar), 23% (Amsterdam), or 65% (break-even at $5/t)

New Bicycle Tonnes Cost Per CDM Value

Mode Share CO2 tonne CO2 ($5/tonne CO2)

3% 23,500 $279 $ 117,300

6% 85,600 $76 $ 427,800

23% 476,100 $14 $2,380,300

65% 1,308,800 $ 5 $6,544,200


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Policy Example: Costs• CDM could offset 2% to 6% of project costs in

the more realistic scenarios (3%, 6% mode share)

– Higher if CER value > $5/tonne– Higher if consider longer project lifetime (14, 21 yrs)

• Costs could be lower if same bike use could be achieved with fewer km of bikeway

– E.g., less expensive bike lanes– Promotional campaigns

• Including co-benefits makes bike projects more attractive from a societal perspective


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Initial Conclusions• Individual bikeways not viable as a CDM project

given current rules and expected credit values– Bundling of multiple projects may help

• A comprehensive network of segregated bikeways plus (cheaper) bike lanes could potentially work

• Cost-sharing that reflects co-benefits could help make the CDM more viable– e.g., with air quality improvement programs, or other

transportation infrastructure projects

• A revolving loan approach could be used to recycle funds back into projects when CDM credits are sold


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Addressing Uncertainty

• Quantifying emissions impacts of bicycle projects and policies is difficult

• Can conservative assumptions minimize uncertainty enough to attract investors and to gain approval of the EB/Meth panel?

• Discounting of emissions benefits may be appropriate

• Small-scale project methodologies allow for streamlining– simplified baseline and monitoring requirements– lower transaction costs


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Innovative Ideas (heretical?)• Official Development Assistance (ODA) cannot be used

for CDM projects• Perhaps demand side projects require special treatment• ODA could make sense to support basic data collection

and monitoring as part of a broader sustainability strategy• It has been observed that provision of infrastructure does

not guarantee use – Promotional campaigns may be key to increasing bike use

(Ortuzar, GEF)– Land use policies can enable shorter trips suitable for bikes

(Ortuzar) (Land use will be discussed in the next session)

• Could ODA fund bike infrastructure and sell CERs to fund promotional strategy or maintenance??– Can full project impacts be counted if CERs only fund a small

portion?


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High Opportunity Costs• Rapid growth in car ownership and use appears inevitable• Availability of efficient options such as bicycle infrastructure

will require deliberate planning and investment• Current infrastructure and investment and development

decisions have a major impact on future emissions• Developing bicycle networks now can advance multiple

sustainability goals – Consider building bike lanes into road maintenance and construction

• There are high opportunity costs for not investing in efficient modes bicycle, pedestrian, transit and sustainable land use Puts the world on high-GHG pathway!


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Closing Challenge• Rapid growth in driving continues to outpace vehicle

efficiency improvements• If the CDM cannot significantly advance non-motorized

modes then other policy mechanisms will be necessary

90%

100%

110%

120%

130%

140%

150%

160%

170%

180%

2000 2005 2010 2015 2020 2025

2000

= 1

00%

Vehicle Miles Traveled

CO2 Emissions

Fuel Economy (f leet)

Source: US DOE, EIA "AEO 2004"

(US data)


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Respondents

• Cesar Garrido, CONASET

• Dr. Juan de Dios Ortúzar, Universidad Catolica de Chile

• Ricardo Montezuma, Fundación Ciudad Humana


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Cesar GarridoCONASET

Implementation of CDM Bike Projects in Santiago

• Policy context: brief overview of bike policies in Santiago• How can CDM consideration be incorporated into the next

bike project or policy?• Can you foresee the CDM helping to overcome some of

barriers to bike lane development in Santiago? What do you see as the biggest hurdles?

• Can monitoring be built into any existing initiatives?• What will it take to achieve significant bicycle use in

Santiago?


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Prof. Juan de Dios OrtúzarUniversidad Catolica de Chile

Methodological Issues

• Accuracy of O-D bicycle data?• Reliability of bicycle demand forecasting approaches?• Practicality of dynamic baselines?• Improvements on avoided emissions calculation?• What level of monitoring is credible? Reasonable?


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Ricardo Montezuma Fundación Ciudad Humana

Replicability of Case Study to Bogotá, Columbia

• Bogotá experience, plans and needs for- monitoring bicycle use- promoting bicycle use

• Thoughts on sufficiency of modeling capability, monitoring resources and data quality for assessing bicycle project impacts

Documents

Assessing how the Clean Development Mechanism can Increase Bicycle Use in Santiago