Transport Emissions Evaluation Model for Projects (TEEMP ... · • TEEMP – Transport Emissions Evaluation Model for Projects (can be pronounced as “temp” or temporary • Excel-based

Transport Emissions Evaluation Model for Projects (TEEMP) : Scenario Example Alvin Mejia Program Manager Low Emissions Urban Development Clean Air Asia

Training on Quantifying Urban Transport GHG Emissions

19  May  2014  

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Mission: to promote better air quality and livable cities by translating knowledge to policies and actions that reduce air pollution and greenhouse gas emissions from transport, energy and other sectors

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Challenges in Transport CO2 Estimation

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7371

Transport Others

As of end of 2013

§ Unique nature of mobile sources of emissions in the transport sector

§ Patchy and insufficient data § Complexity and cost of data

collection methods (as required by some mechanisms)

§ Availability of suitable modeling tools

§ Geographical boundaries setting and estimating leakages

§ Difficulties in ensuring endurance of emissions reductions

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CDM – AM0031 :BRT – Data Required Data  variable   Recording

frequency Propor7on  of  data  to  be  monitored

Number  of  vehicles Before  project  start  and  annually  (in  the  case  of  modal  shiA  for  passenger  cars)  

100%  and  annually  based  on  a  survey  of  passengers  using  the  new  system

Fuel  efficiency Before  project  start Sample Total  distance  driven  by  all  vehicles  in  category Before  project  start  and  parJally  annually Sample

Passengers  transported  baseline  by  vehicle  category  I Before  project  start 100%

Average  occupancy  rate  baseline  of  vehicle  category  I Before  project  start  and  for  buses  and  taxis  minimum  year  3,  6  and  10

Sample

Average  trip  distance  baseline  for  vehicle  category  I Before  project  start  and  annually  (in  the  case  of  modal  shiA  for  passenger  cars)

Sample  and  sample  survey

Total  fuel  consumpJon  per  vehicle  category Before  project  start Sample

Passengers  transported  by  project Annually 100% Share  of  passengers  that  would  have  taken  transport  mode  I

Annually Sample  survey

Passengers  transported  by  project  who  would  have  used  transport  mode  i

Bi-­‐monthly   Sample  survey

Policies  that  affect  baseline   Before  project  start  and  annually 100%

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CDM : BRT – Bogota Transmilenio     TransMilenio  Phase  II  to  IV  

    Monitoring  2006   Monitoring    2008   Monitoring  2009   Monitoring  2010  

    Actual     Expected   Actual    Expected   Actual    Expected   Actual     Expected  Passengers  transported  by  project  (million)  

94   147   118   356   134   478   149   478  

Share  of  passengers  which  would  have  used  passenger  cars  (%)  

4.3   5.5   2.4   5.5   2.1   5.5   2.6   5.5  

Share  of  passengers  which  would  have  used  taxis  (%)  

5.5   5.6   5.5   5.6   4.8   5.6   5   5.6  

Share  of  passengers  which  would  have  used  buses  (%)  

89.1   88   91.4   88   92.5   88   91.6   88  

Share  of  passengers  which  would  have  used  NMT  or  not  made  the  trip  (%)  

1.1   0.8   0.7   0.8   0.6   0.8   0.7   0.8  

Emission  reducJons   -­‐40%   -­‐70%   -­‐74%   -­‐74%  BRT Bogotá, Colombia: TransMilenio Phase II To IV (monitoring report 2010)

7  Clean Air Asia, 2012

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Introduction to TEEMP •  TEEMP – Transport Emissions

Evaluation Model for Projects (can be pronounced as “temp” or temporary

•  Excel-based tools that enable the quantification the CO2 (and other) impacts of transport projects

●  Commuter strategies ●  Pricing strategies ●  Eco-driving ●  PAYD insurance ●  TEEMP City

●  BRT ●  MRT ●  Railways ●  Roads ●  Walkability improvement ●  Bike sharing ●  Bikeways ●  Vehicle replacement

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Basic Concepts: BAU vs Mitigation Scenario

Business-as usual scenario

“Project” scenario

CO

2 Em

issi

ons

Time

Emissions savings

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Sample Indicators : Based on ADB Application of TEEMP

DescripJon  CO2  Savings  Indicator  

(ton  per  unit)    

unit  

Expressway   -­‐700   ton/km/year  Bikeway   250   ton/km/year  

Rural  Road  (capacity)   0   ton/km/year  Rural  Road  (RehabilitaJon)   10   ton/km/year  

Metro/Monorail   6200   ton/km/year  BRTS   5000   ton/km/year  

Railway   2900   ton/km/year  Urban  Road  2  lane  to  4  lane   -­‐400   ton/km/year  Urban  Road  4  lane  to  6  lane   -­‐200   ton/km/year  

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Case Study: Pasig City E-trike Project

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TEEMP : VEHICLE REPLACEMENT TOOL •  Can be used for estimating

the impacts of replacing older vehicles in a fleet with new ones (fuels used can be defined)

•  Can be used for assessing the replacement of fossil-fuelled vehicles with electric-driven ones

•  Impacts assessment include •  Energy •  Emissions (CO2) •  Costs (maintenance, replacement,

fuel costs)

Reference Scenario - Input •  Users can input individual vehicles

to be replaced, or a group of vehicles (depending on the data at hand)

•  Input data include •  Year when the vehicles will be

replaced •  Number of units to be replaced •  Fuel type of the vehicles to be

replaced •  Kilometers per day done by the

vehicles •  Average load •  Fuel consumption/efficiencies •  Number of days operating in a year

•  Fuels –definition of the different fuels involved

•  Electricity grid mix – to derive the emission factor of the grid (if electric vehicles are involved)

•  Fuel efficiencies of alternative vehicles – if data is not available, default data is provided

•  Costs : maintenance, fuel prices, inflation assumptions, costs of alternative vehicles

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Case Study : Electric Tricycles in Pasig City, Philippines

•  The City Government of Pasig has initiated a pilot project involving the TODA (tricycle operators and drivers association) of San Nicolas (SNTODA).

•  The pilot project involves the replacement of an initial 26 gasoline-powered tricycles with electric units.

•  The City Government will support the replacement of the gasoline-powered tricycles through a 0% loan for the drivers who will avail of the electric units.

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Tricycles in Pasig City

•  There are around 8,519 tricycles (with legal franchises) plying the streets of Pasig City

•  An estimated 2,000 additional units are “colorum”

•  Many are running on 2-stroke motorcycle units that emit high levels of particulate matter, VOCs, and also contribute to CO2 emissions particularly to their low efficiencies

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Sample Survey : Salient Points

•  A survey involving the intended beneficiaries of the electric tricycle scheme was conducted to support the case for the project

•  The partial survey results show that the average age of the vehicles to be replaced is 8.62 years

•  Without the project, the beneficiaries stated that on average, the current vehicles will have an maximum service life of 18.36 years

•  This means that for the next ten years, on average, these vehicles will still be plying the streets, if the project was not put in place.

•  Average Vehicle-kilometres/day : 34

•  Average number of days operating a week: 6.5

•  Average earnings a day: 10 USD

•  Average number of trips a day : 33

•  Average occupancy : 3

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CO2 Reduction (Static)

•  If we assume a fairly static reference scenario and do not consider fuel efficiency degradation and the replacement that is ought to happen anyway without the project, the results of the simulation are:

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Tons  CO2  

Electric  

Other3  

Other2  

Other1  

CNG  

LPG  

Diesel  

Gasoline  

Project  

Average  per  year  (tons  CO2)  

Average/year/unit  (tons  CO2)  

Reference  Scenario   53.22                                                2.04    

Project  Scenario   8.99                                                0.35    

Savings   44.24                                                1.7    

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CO2 Reduction (Dynamic)

•  In the dynamic scenario, efficiency degradation (% decrease per year) were assumed for both the baseline and project scenarios. Also, it is assumed that for both of the scenarios, eventual vehicle replacements will take place.

Average  per  year  (tons  CO2)  

Average/year/unit  (tons  CO2)  

Reference  Scenario   46.05                                                1.77  

Project  Scenario   9.74                                                0.37    

Savings   36.31                                                1.4     0  

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Tons  CO2    

Electric  

Other3  

Other2  

Other1  

CNG  

LPG  

Diesel  

Gasoline  

Project  

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Rolling Out •  The City Government Of Pasig is

implementing the pilot project as a means to test the technology and whether it will work in local conditions. There is definitely an interest from the City Government for wider adoption of electric tricycles in the future once it is tested to be viable. A scenario was ran assuming a 50% electric tricycle share in 20 years.

•  Reaching the target in 20 years would mean replacing an additional 205 units per year up to 2034, which is viable. This equates to a 16% annual growth rate in a span of 20 years (4309 electric tricycles by 2034).

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2014  2016  2018  2020  2022  2024  2026  2028  2030  2032  

Tons  CO2  

Electric  

Other3  

Other2  

Other1  

CNG  

LPG  

Diesel  

Gasoline  

Project  

834 thousand liters of gasoline per year (20-year average), which equates to about 5.2 million USD per year average (20 years) yearly savings on fuel costs.

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Reflections and Insights

§  Transportation is a unique sector when it comes to data (availability, reliability and quality) particularly in developing cities

§  Enabling quantification of impacts (not just CO2) is necessary and will be useful in convincing policymakers at the ground level

§  Borrowing default data (proxy activity and emission factors) is most of the times unavoidable, but these have to be transparent. Rules may perhaps be relaxed (for future MRV systems) for ex-ante calculations, to enable access to funds, but monitoring can be more stringent.

§  Tools for and capacity building on emissions impact quantification are much needed in developing cities and countries

§  Building the case for monitoring data parameters (in relation to other uses) must be made

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Other Tools for CO2 Impacts Quantification

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Green Trucks Toolkit •  This toolkit is a simple excel-based model which can be

used to estimate the baseline impacts of a fleet as well as evaluate the impacts of appplying different technologies and strategies that improve vehicle efficienies and/or reduce pollution. Objectives

The tool was developed with the following objectives in mind: •  Enable users, particularly truck fleet

managers, to roughly estimate the air pollution and greenhouse gas emission impacts of their fleets

•  Raise awareness on the strategies that managers of truck fleets can use in order to reduce the environmental impacts of their fleets and increase their fleets’ efficiencies

•  Enable managers of truck fleets to roughly estimate the costs and benefi ts of implementing such strategies.

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Backcasting Tool (soon to be released) •  Developed by

ITPS, in cooperation with Clean Air Asia and local experts

•  National level mitigation analysis of avoid-shift-improve policy packages

•  Initial application-10 Southeast Asian Countries

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FreightShip  

FreightAir  

FreightRail  

Truck  

PassShip  

PassAir  

PassRail  

2W/3W  

Small  buses  

Large  bus  

Jeeps  

Pickups  

Car  

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thou

sand

 tons  CO2  

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Cleanairinitiative.org

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Clean Air Asia Country Networks

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Clean Air Asia Center Members

• Shell • Asia Clean Fuels Association • Corning

240 Clean Air Asia Partnership Members

•  Cities •  Environment ministries and government

agencies •  Development agencies and foundations •  Non-government organizations •  Academic and research institutions •  Private sector companies and

associations

Donors in 2012 to 2013 Asian Development Bank l Cities Development Initiative for Asia l ClimateWorks Foundation l DHL/IKEA/UPS l Energy Foundation l Fredskorpset Norway l Fu Tak Iam Foundation l German International Cooperation (GIZ) l Institute for Global Environmental Strategies (IGES) l Institute for Transport Policy Studies l Institute for Transportation and Development Policy l International Union for Conservation of Nature l L'Agence Française de Développement (AFD) l MAHA l Pilipinas Shell l

Rockefeller Brothers Fund l Shakti Foundation l Shell Foundation l United Nations Environment Program Partnership for Clean Fuels and Vehicles (UNEP PCFV) l USAID CEnergy l Veolia l World Bank

For more information: www.cleanairasia.org For more information: www.cleanairasia.org

[email protected]