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2011 Jürg M. Grütter GHG Methodology for Efficiency Improvements HDVs and Mobile Machinery
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Page 1: Canada VCS freight meth version 1 3 (2)

2011

Jürg M. Grütter

GHG Methodology for Efficiency Improvements HDVs and Mobile

Machinery

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Methodology based on VCS 2011 Format for GHG Methodologies

Title Date of Issue GHG Methodology for Efficiency Improvements HDVs and Mobile Machinery

31/01/2011

Version Sectoral Scope 1.3 7 Element Type Prepared by Methodology Grütter Consulting AG

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Relationship to Approved or Pending Methodologies

The methodology takes components from the approved CDM methodology ACM0016 “Baseline Methodology for Mass Rapid Transit Projects” developed also by Grütter Consulting. The components used refer basically to calculation of GHG emissions resulting from fuel combustion.

No approved or pending methodology at the VCS or the CDM exists which is commensurate with the proposed project activity thus requiring a new methodology.

Table 1: List of Approved or Pending Methodologies Sectoral Scope 7 (Transport) Reference Number

Title GHG Program

Relation to new methodology

AM0031 Baseline Methodology for Bus Rapid Transit Projects

CDM Methodology is for public transit activities including mode switch and thus not compatible with proposed new methodology. Thus the procedure for baseline determination is completely different.

ACM0016 Baseline Methodology for Mass Rapid Transit Projects

CDM Methodology is for public transit activities including mode switch and thus not compatible with proposed new methodology. Thus the procedure for baseline determination is completely different.

AM0090 Modal shift in transportation of cargo from road transportation to water or rail transportation

CDM Methodology is for freight modal shift and thus a different area than the proposed new methodology. Thus the procedure for baseline determination is completely different.

AMS.III.C Emission reductions by electric and hybrid vehicles

CDM The methodology is for switching from fossil to electric or hybrid vehicles and not for efficiency gains. Thus the procedure for baseline determination is completely different.

AMS.III.S Introduction of low-emission vehicles / technologies to commercial vehicle fleets

CDM Methodology for fixed route vehicles, only for small scale projects and not for machinery. The procedure for determination of the baseline and project emissions is thus different in the new methodology.

AMS.III.T Plant oil production and use for transport applications

CDM The methodology is for the production of bio-fuel and thus a different area than the proposed methodology.

AMS.II.IU Cable Cars for Mass Rapid Transit System (MRTS)

CDM The methodology is for the establishment of cable car based mass transit systems and thus a different area than the proposed methodology. Thus the procedure for baseline determination is completely different.

AMS.III.AA Transportation energy efficiency activities using retrofit technologies

CDM Methodology for passenger vehicles and for single type measures. The procedure for determination of the baseline and project emissions is thus different in the new methodology.

AMS.III.AK Biodiesel production and use for transport application

CDM The methodology is for the production of bio-fuel and thus a different area than the proposed methodology.

AMS.III.AP Transport energy efficiency activities using post-fit idling stop device

CDM The methodology is applicable to one type of device only and for passenger transport. The procedure for determining baseline emissions is thus not compatible with the proposed new methodology.

AMS.III.AQ Introduction of Bio-CNG in transportation applications

CDM Methodology for fuel switch to bio-fuel and thus different area than the proposed methodology.

SSC.NM.0 Transport energy efficiency CDM The methodology is applicable to one type of device

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61 activities installing digital tachograph systems to commercial freight transport fleets

(approval pending)

only. The procedure for determining baseline emissions is thus not compatible with the proposed new methodology.

Methodology for determining GHG emission reductions through bicycle sharing projects

VCS (approval pending)

The methodology is applicable to mode shift towards bicycles and thus another area than the proposed new methodology.

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Table of Contents

Relationship to Approved or Pending Methodologies ............................................................................ 3

Table of Contents .................................................................................................................................... 5

1. Sources ............................................................................................................................................ 6

2. Summary Description of the Methodology ..................................................................................... 6

3. Definitions ....................................................................................................................................... 6

4. Applicability Conditions ................................................................................................................... 7

5. Project Boundary ............................................................................................................................. 8

6. Procedure for Determining the Baseline Scenario .......................................................................... 8

7. Procedure for Demonstrating Additionality .................................................................................. 11

8. Baseline Emissions ......................................................................................................................... 13

8.1. Baseline Emissions for Trucks ................................................................................................ 14

8.2. Baseline Emissions for Mobile Equipment and Machinery ................................................... 16

9. Project Emissions ........................................................................................................................... 20

10. Leakage ...................................................................................................................................... 22

11. Quantification of Net GHG Emission Reduction ........................................................................ 24

12. Data and Parameters not Monitored ........................................................................................ 24

13. Monitoring Description ............................................................................................................. 27

14. Data and Parameters Monitored .............................................................................................. 27

15. References and Other Information ........................................................................................... 30

Annex 1: Default Baseline Emission Factor Forestry ............................................................................. 31

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1. Sources

The methodology uses as sources:

• UNFCCC, ACM0016 “Baseline Methodology for Mass Rapid Transit Projects” • UNFCCC, General Guidelines for Sampling and Surveys for Small-Scale CDM Project Activities,

EB 50 Annex 30

2. Summary Description of the Methodology

The methodology is for project activities which improve the efficiency of trucks and/or mobile machinery equipment e.g. bulldozers, road-building machines etc. Measures to improve operating vehicle efficiency may include but are not limited to anti-idling devices, eco-drive, tire-rolling resistance improvement, air-conditioning system improvement, low viscosity oils, cab-heaters, aerodynamic drag reduction measures, transmission improvements, etc.

Baseline emissions are based on a pre-calculated baseline emission factor per activity units defined as gross ton-kilometer for trucks and defined by the project for machinery/equipment. In a first step the sub-category per activity indicator (e.g. freight type) is defined and for latter the lower 95% of a baseline sample is calculated based on monitored data or on registered default values. The activity level is monitored by the project.

Project emissions are based on the fuel consumed by project units.

Emission reductions are baseline minus project emissions which technically correspond to the difference of baseline and project emissions per unit of activity multiplied with the project activity level. This reflects in a conservative manner the efficiency gains due to the project.

The methodology was developed by Grütter Consulting (www.transport-ghg.com) on behalf of 0896996BC Ltd., Canada.

3. Definitions

Definitions as used in the context of this methodology include:

• Ton-Kilometers are net tons multiplied with the distance driven. • Net tons are the freight weight excluding the truck weight. • Gross tons are the gross vehicle weight including vehicle plus freight loaded.

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Acronyms

CDM Clean Development Mechanism COAC Carbon Offset Aggregation Co-op EB Executive Board of the UNFCCC EF Emission Factor GHG Greenhouse Gases GVWR Gross Vehicle Weight Rating HDV Heavy Duty Vehicles IPCC Intergovernmental Panel on Climate Change IRR Internal Rate of Return NCV Net Calorific Value PCT Pacific Carbon Trust PD Project Document RFID Radio Frequency Identification SD Standard Deviation tkm Ton-Kilometer UNFCCC United Nations Framework on Climate Change Convention WACC Weighted Average Cost of Capital

4. Applicability Conditions

The methodology is applicable to efficiency improvements of heavy duty trucks and mobile equipment such as forklifts, graders, cats, road-building machines etc.

The methodology is not applicable for a fuel switch from fossil towards bio-fuels. However the project units can use the same blend of bio-fuel as used also in the baseline. In the case of bio-fuel blends the bio-fuel share is accounted for as 0-emission. This is conservative as overall fuel savings occur with the project and the blend share remains constant.

The methodology is applicable if fuel switch e.g. from liquid to gaseous fuels occurs. This is basically to remain flexible as such shifts can occur over time. However the methodology is designed as an efficiency improvement methodology and not as a fuel-switch methodology. Therefore the methodology can only be used if fuel switch is not more than 20% of total used fuel measured in the respective year compared to the baseline situation (MJ of fuel switched in year y / MJ total fuel used year y).

The methodology is not applicable to modal shift e.g. moving goods from truck to rail.

The methodology is not applicable for electricity usage.

Finally, this methodology is only applicable if the application of the procedure to identify the baseline scenario results in that a continuation of the current system is the most plausible baseline scenario.

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5. Project Boundary

The spatial extent of the project boundary encompasses the area in which the project takes place. It is based on the origins and destinations of freight transported by the project system or sites in which project equipment is used.

The greenhouse gases included in or excluded from the project boundary are shown in Table 1.

Table 1: Emissions Sources Included in or Excluded from the Project Boundary

Source Gas Included? Justification/Explanation

Base

line

Scen

ario

Emissions of trucks and

mobile equipment

CO2 Yes Major emission source

CH4 Yes Included only if gaseous fuels are used. Vehicle tailpipe CH4 emissions are excluded for liquid fuels.

Combined CH4 and N2O emissions make less than 2% of total CO2eq emissions in diesel engines. Its omittance in diesel powered engines in baseline as well as project emissions is conservative as fuel consumption and thus also CH4 emissions are reduced through the project.

N2O No Combined CH4 and N2O emissions make less than 2% of total CO2eq emissions in diesel engines. Its omittance in baseline as well as project emissions is conservative as fuel consumption and thus also N2O emissions are reduced through the project.

others No No emission of PFCs, HFCs or SF6 due to fuel consumption.

Proj

ect A

ctiv

ity

Emissions of trucks and

mobile equipment

CO2 Yes Major emission source

CH4 Yes Included only if gaseous fuels are used. See argument above

N2O No See argument above

others No See argument above

6. Procedure for Determining the Baseline Scenario

Specific types of potential baseline approaches are evaluated:

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• Historic Benchmark: This approach assumes that past trends in emissions will continue into the future.

• Performance Standard: This approach assumes that the typical emissions profile for the sector is a reasonable representation of the baseline.

• Comparison-based: This approach compares project emissions with those of a control group. • Projection-based: This approach uses forecasting techniques to determine baseline

emissions. • Pre-registered: Standards documented in related Protocols or methodologies.

The suitability of each baseline approach is discussed in Table 2

Table 2: Baseline Approach and their Suitability Approach Suitability Historic Benchmark Baseline emissions are determined based on past practices of the

project proponent. This approach is commonly used in transport and is the approach used for all approved CDM methodologies in the transport sector. An historic, project proponent-specific baseline offers a good level of transparency and accuracy for the baseline. To avoid seasonal variations baseline data should be compiled over minimum 1 year. Also data used should not be elder than 3 years.

Performance Standard A performance standard approach would generally be less accurate than other potential baseline approaches, since it is based on a sector standard assumptions and data, versus project-specific data. Performance standards can reasonably well be developed for homogeneous sectors e.g. passenger cars. However this methodology is for very heterogeneous applications. Emission levels in terms of GHG emissions per tkm between trucks used for different applications (type of cargo as well as average distance driven1) are huge. Given the range of project activities and conditions that fall within the scope of this methodology, the development of a performance standard is difficult from a data availability perspective and would require a number of different performance standards for different project sub-types. The approach of a performance standard can however be realized as a bottom-up approach using historic benchmarks developed by project proponents for specific sub-sectors. Such standards could then be used by othr project proponents in the same sub-sector.

Comparison-Based Operations of the project proponent during the project would be compared to the activities of other similar firms not undertaking project activities (i.e. a control group). This approach has various theoretical and practical shortcomings: a). The use of the control group approach is common in experimental and quasi-experimental research designs, when the purpose is to evaluate the effects of some form of intervention on a population of interest. Quasi-experimental designs which do not depend on randomized placement of observations, but instead use a number of

1 For example short-haul delivery trucks running in cities will have far higher emission levels per tkm than long-haul overland trucks; for the incidence of different freight goods see e.g. approved CDM methodology AM0090 table 2

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approaches to control for factors other than the specific intervention that might influence the outcome(s) of interest could theoretically be used. A major challenge in quasi-experimental evaluations of this type comes from the possibility to falsely infer causality; that is, attributing a difference in outcomes to the intervention when, in fact, the difference in outcomes is actually due to unobserved characteristics. b). In practice data will not be readily available from a “control-group” operating under similar circumstances in a similar region. Company internal “control-groups” might not be considered as neutral and trustworthy as an interested party is involved and data from company external “control-groups” being potentially competitors would not be readily available. Basically this approach is thus infeasible due to lack of data access and possibility to monitor, the problem how to ensure that the control group would follow business-as-usual practices relevant to the project proponent‟s specific situation and business reality and the problem of differentiating other influence factors of emissions of the control group which do not affect in the same manner or magnitude the project proponent.

Projection-Based The business-as-usual case would be projected into the future. Basically this approach is very similar to the historic approach but including an appraisal of changes which would occur also in absence of the project in the future. Two aspects are considered:

• Changes which improve the performance of HDVs and Mobile Machinery in absence of project interventions. This aspect is discussed in chapter 8 (Baseline Emissions) under the title of “technology improvement factor” i.e. an independent BAU improvement factored into the baseline. This can be realized on a dynamic base or be a fixed factor valid for a certain time-period.

• Project interventions which would also occur under BAU i.e. in absence of the project. This point is discussed under the chapter 7 (Additionality). If project interventions would also occur in absence of the project then latter is not additional and the project is the baseline. If project interventions face barriers or are financially non-viable in absence of carbon finance then they are not BAU and can thus not be factored inside BAU projections.

Pre-registered This approach would be valid for performance standards for sub-sectors as included in this methodology or as determined in registered projects.

Based on above assessment, the Historic Benchmark approach has been selected as the most appropriate baseline scenario. The Historic Benchmark is combined with projections for independent BAU improvement based on a technology improvement factor and thus factor in an important element of the Projection-Based Approach. The Historic Benchmark approach is also used to generate in a bottom-up manner sub-sectoral performance standards which can then be used by other project proponents inside the same sub-sector as well as for Greenfield projects where no historic data is available.

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The development of the historic benchmark including the technology improvement factor is detailed in chapter 8.

The proof that project interventions are not BAU is realized in the following chapter 7.

7. Procedure for Demonstrating Additionality

Additionality shall be determined by using either:

1. The most recent version of the, “Tool for the demonstration and assessment of additionality” as approved by the EB of the CDM;

2. The approach as described in the most recent approved version of the Voluntary Carbon Standard.

3. The approach using the additionality toolkit developed by the Pacific Carbon Trust. 4. Technological additionality

Option 1: CDM Tool

The following parts are a short summary of this tool. See for details the referenced tool.

Step 2 or step 3 must be performed. If step 2 shows that the project is financially feasible in absence of carbon finance then step 3 can be performed.

If step 2 shows that the project is not financially feasible in absence of carbon finance and if step 4 shows that the project is not common practice then it is considered as additional.

STEP 1. Identification of alternatives to the project activity consistent with mandatory laws

and regulations

STEP 2. Investment analysis

STEP 3. Barrier analysis

STEP 4. Common practice analysis

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If step 3 shows that the project faces at least one barrier which prevents the implementation of the project in absence of carbon finance and if step 4 shows that the project is not common practice then it is considered as additional.

If the CDM tool is used then the corresponding latest versions of guidelines of the CDM, e.g. guideline on determination of WACC (Weighted Average Cost of Capital) or guideline on the assessment of investment analysis, are applicable.

Option 2: VCS Tool

The VCS approach includes the usage of:

• Project test including as steps: o Step 1: Regulatory Surplus o Step 2: Implementation Barriers including investment barriers, technological barriers

and/or institutional barriers o Step 3: Common Practice

• Performance test including as steps o Step 1: Regulatory Surplus o Step 2: Performance Standard for the specific activity type as defined in the default

values of the methodology

For more details see the VCS guidelines on additionality.

Option 3: PCT Tool

A summary of the tool is provided.

In the 3rd step following barriers are identified in the referenced tool:

• Investment barrier • Technology barrier • Social barrier • Another type of barrier

The project is additional if the potential can be identified, the baseline is comparable to the project and if at least one barrier prevents the project from being the baseline and this barrier can be overcome fully or partially with carbon finance.

For major details see the Guidance document to determine project additionality under http://www.pacificcarbontrust.com/LinkClick.aspx?fileticket=s9emUpzIXoo%3d&tabid=80&mid=572

Step 1: Identify potential

Step 3: Identify barrier that prevents the project from being the baseline

Step 2: Evaluate comparability of baseline to project

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Option 4: Technological Additionality

For technological additionality evidence can be sourced publicly, or third party assured, to support the assertion of technological additionality and could include:

• Internal official decision making documents demonstrating that the technology pursued is not the lowest cost (lifecycle basis) economic option for the organization.

• An independent study or analysis of publicly available information demonstrating the ‘penetration rate’ of the technology in question within a relevant geographic example.

8. Baseline Emissions

Baseline emissions are calculated separately for mobile equipment and for HDVs.

Fuel policy changes e.g. the promulgation of low carbon fuel standards are included in the Emission Factor i.e. the project does not receive emission reductions for changes in fuel policies leading to reduced carbon emissions from fuels such as e.g. a compulsory blending of bio-fuels. In such cases the Emission Factor for the project as well as for the baseline is adjusted in the same ratio e.g. if a 5% blend by energy content is realized then the Baseline EF as well as the Project EF are multiplied with the factor 0.95 to account for the bio-fuel share.

Baseline emissions and project emissions are calculated the same manner. The differences are:

• The baseline emission factor is determined ex-ante. Its calculation is however idem to project emissions.

• The baseline emission factor is a historical baseline combined with a technology improvement factor. Again however the calculation procedure is identical

The only difference between baseline and project emission calculations is that for the sake of a conservative approach in the case of determination of the baseline emission factor the lower 95% confidence interval is taken and in case of the project the upper 95% confidence interval is used. This restriction applies if project emissions are based on samples and not on a full data collection. This restriction is realized to provide for conservative estimations of emission reductions and to eliminate giving credits caused by random variations.

In chapter 8.1. potential baseline emission sources are identified. Thereafter the procedure to calculate baseline emissions is determined. In a third step the technological improvement factor is defined.

8.1. Identification of Baseline Emission Sources

Baseline idem to project emission sources are those that are under control of the project. Leakage emission sources are those that are caused indirectly by the project activity but are not under its

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control. This refers e.g. to upstream emissions resulting from truck construction. See for a discussion of leakage emissions chapter 10.

To assess the relevance for baseline emission sources, the following criteria were used:

• No change between project and baseline: if equivalent emission sources do not change between the project and the baseline then this source is excluded as it would have no influence on emission reductions.

• Emissions greater for baseline than project: if estimated baseline emissions are larger than estimated equivalent project emissions then this source can be excluded2. This is a conservative approach.

The following table presents the baseline emission sources and a discussion of their relevance. For non-relevant parameters a justification is provided. Monitoring or estimation of relevant data parameters are discussed further in the chapter 8.2.

Table 3: Baseline Emission Sources Emission Source Relevance Justification B1: HDV/Mobile machinery loading and unloading

Not relevant

No change between project and baseline as project activities do not include loading or unloading

B2: HDV/Mobile machinery operation

relevant Most important emission source; This is monitored; see 8.2.

B3: HDV/Mobile machinery maintenance

Not relevant

No change between project and baseline; equipment used does not affect HDV/Mobile machinery maintenance as latter is basically related to mileage and/or equipment age which is not influenced by the project activities.

Upstream and downstream emissions include:

• Equipment construction and decommissioning; • Upstream fuel emissions from extraction, processing and transport; • Transport infrastructure construction and maintenance;

These emission sources are not included as baseline but as leakage emission sources (see chapter 10).

8.2. Quantification of Baseline Emissions

8.2.1. Quantification of Baseline Emissions for HDVs

Baseline emissions are calculated based on the baseline emission factor per gross-tkm fixed prior project start multiplied with the actual gross-tkm performed with the project activity.

2 Exclusion is e.g. done due to lack of precise data availability or costs involved to collect data being higher than expected benefit from emission reductions associated to this source

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∑ ×=i

yitkmiTKMy ALEFBE ,,, (1)

Where: BEy Baseline emissions in the year y (tCO2) EFTKM,i Emission factor per gross ton-kilometre of truck transporting freight type i

(gCO2/tkm) ALtkm,,y Activity level of project in terms of gross tkm of freight type i transported by the

project system in the year y (tkm) Indicator gross tkm

The indicator is based on gross and not net tkm. In many cases net tkm are not available and gross tkm (includes truck weight) are more reliable. Also as the purpose of this methodology is not mode shift (in latter case net tkm would be better) but efficiency improvements, the indicator gross tkm is appropriate. It is also the indicator used with success e.g. in the domestic Swiss GHG abatement program with participation of around 100 companies which perform efficiency improvements and monitor and report these. Based on the recorded improvements transport companies receive since the year 2002 income from the sale of monitored GHG reductions. This methodological approach was approved by the Swiss Ministry of Environment as well as the Swiss Department of Energy and has proven to be practical, straightforward and trustworthy.

Freight types

The activity level for each freight type is based on company records. Freight types shall be differentiated basically according to types of trucks used and their specific weight. Freight categories proposed include:

• Container transport • Liquids e.g. fuel transport • Cement, steel, stones, ore etc • Food and agricultural products • Manufactured products including cars, machinery and others • Forestry industry including sawmills, log transporting etc

Emission Factor

The baseline emission factor (EFTKM,i) for transportation of cargo should be determined using an already approved benchmark/default value or determining in the PD a benchmark/default value. The project proponent can also use benchmarks/default values calculated in registered PDs using this methodology.

Default factors can be calculated based on samples monitoring gross tkm and fuel usage plus fuel type over minimum 1 year (to include seasonal variations). The lower boundary of the 95% confidence interval should have a deviation of less than 10% from the average value to ensure a certain homogeneousness of data. If this is not achieved then it is advisable to form subgroups of freight/truck categories using criteria such as

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• Routes or route types (highway, urban) • Average trip distance (long-haul, medium-haul, short-haul) • Average gross vehicle weight • Further differentiation of freight type

While differences between trucks, drivers, truck makes and routes will always exist the objective is to have benchmarks as homogenous as possible to ensure that also relatively small efficiency improvements can be measured and that changes in CO2 emissions can be attributed with a reasonable precision to the project activity muting out background statistical “noise” or variations which occur also in absence of the project.

The sample size used must be sufficient to ensure a 95% confidence interval with a 5% error boundary using the following formula:

Minimum Sample Size = (Standard Deviation * 1.96 / (average value * maximum error bound)) ^ 2

The value of 1.96 is based on the z-distribution for a 95% confidence interval The maximum error bound is 0.05 The SD (Standard Deviation) is based on the sample

The benchmark/default factor is the lower 95% confidence interval of the value found using for samples below 50 units the Student’s t-test and for samples over 50 units the z-test. This is a conservative approach as baseline emissions are thus within a 95% confidence interval equal to or higher than the default value used.

Table 4: Default Baseline Emission Factors Gross tkm per Freight Type i Freight Type i Emission factor in gCO2/gross tkm Forestry industry incl. log transport and associated with trip driving ranges > 15km per trip3

31

Annex 1 of the methodology provides the detailed calculations of this benchmark.

8.2.2. Quantification of Baseline Emissions for Mobile Equipment and Machinery

Baseline emissions are calculated based on the baseline emission factor per ton-hour or per activity level fixed in the PD. The activity level indicator must be justified in the PD and must fulfil the following criteria:

• Higher activity levels must lead to higher fuel consumption. A linear relationship would be ideal, is however not always the case in practice. The relationship can be proven in the PD with theoretical arguments and/or with practical data.

• The activity level must be measurable with an acceptable level of certainty.

3 This condition is included as very small trip distances e.g. inside forests or compounds lead to significantly higher emissions and would thus require a different category

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• Changes in the relation fuel usage to activity level must be related to efficiency or changes of fuel type used. In other words such changes should not be random and due to external factors not under the influence of the project. To proof this case the data from the sample to determine the baseline emission factor at the lower boundary of the 95% confidence interval should have a deviation of less than 20% from the average value to ensure a homogeneousness of data. If this is not achieved then more homogenous subgroups of the machinery/equipment must be made.

• Indicators can be related to the equipment/machinery itself or to the production output e.g. amount of processed material.

The emission factor is fixed prior project start and multiplied with the activity level of the project.

∑ ×=i

yiAIiAIy ALEFBE ,,, (2)

Where: BEy Baseline emissions in the year y (tCO2) EFEA,i Emission factor per activity indicator of equipment/machinery type i (gCO2/xxx) ALAI,,y Activity level of project in terms of activity indicator of equipment/machinery type i

by the project system in the year y (xxx) Emission Factor

The baseline emission factor (EFAI,i) should be determined in the PD. The project proponent can also use benchmarks/default values calculated in registered PDs using this methodology.

Default factors can be calculated based on samples monitoring the activity indicator and the fuel usage plus fuel type over minimum 1 year (to include seasonal variations). The lower boundary of the 95% confidence interval should have a deviation of less than 20% from the average value to ensure a certain homogeneousness of data4. If this is not achieved then it is advisable to form more homogenous subgroups.

The sample size used must be sufficient to ensure a 95% confidence interval with a 5% error boundary using the following formula:

Minimum Sample Size = (Standard Deviation * 1.96 / (average value * maximum error bound)) ^ 2

The value of 1.96 is based on the z-distribution for a 95% confidence interval The maximum error bound is 0.05 The SD (Standard Deviation) is based on the sample

The benchmark/default factor is the lower 95% confidence interval of the value found using for sample below 50 units the Student’s t-test and for samples over 50 units the z-test. This is a conservative approach as baseline emissions are thus within a 95% confidence interval equal to or higher than the default value used.

4 The spread in this case is larger due to less homogeneity of this type of activity in general than for trucks.

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8.3. Technology Improvement Factor

8.3.1. General Aspects

Following points need to be considered when applying the technology improvement factor which corresponds to the establishment of a dynamic baseline or a projection based baseline:

1) The baseline emission factor must be multiplied with the corresponding technology improvement factor which is an annual factor pre defined for the crediting period of the project.

2) The default technology improvement factor defined in this methodology can be used for all projects registered prior 2015. Thereafter the default factor must be updated based on new data. Usage of a constant technology improvement factor for projects presented up to 2015 is justifiable as technology changes between years are: • Not radical i.e. improvements from year to year are marginal; • Fleet renewal takes time i.e. only a certain percentage of the fleet is replaced annually and

thus benefits potentially from technology improvements leading to reduced fuel usage5. 3) In case of legal GHG requirements enforced by national, regional or local authorities (e.g. fleet

GHG emissions) the legally enforced annual GHG improvement factor shall be taken as technology improvement factor.

8.3.2. Technology Improvement Factor HDVs

An annual technology improvement factor would be justified if it can be shown that under Business As Usual i.e. in absence of the project, emissions would reduce anyway due to regular truck replacement. However historical data shows that fuel consumption and therefore emissions of trucks have not improved in the last decade especially since Euro1 (elder trucks i.e. trucks prior 1993 in general do have higher fuel consumption values however as of 2011 their share of trucks in usage is marginal and even more so during project implementation period in the next decade). This is due not least to a focus on local pollutants especially PM and NOx which have led to a certain trade-off with fuel consumption values.

Graph 1 shows the relation Specific Fuel Consumption and truck age of a sample of more than 1,400 trucks based on continuous measurements of tkm and SFC realized during one year on numerous routes, with a large variety of truck brands and with many different drivers thus representing a very large sample of a variety of trucks, routes, drivers and cargos. Especially important is the fact that this data is based on actual road-performance and not on drive-cycle laboratory results. No correlation was found between truck age and SFC i.e. fuel consumption variations are dependent on other factors such as trip structure, road characteristics, load, driver, vehicle brand etc but not on the vehicle age. This is also true for Euro 4 or Euro 5 trucks. Fuel consumption changes depend in these cases on the technology choice, e.g., using Selective Catalytic Reduction (SCR)-Technology combined with AdBlue (which depending on production pattern can be considered as bio-fuel additive) or AGR 5 If for example a new technology leads to a 5% specific fuel reduction and trucks are replaced over 10 years then the annual fleet improvement due to the introduction of the new technology is 0.5% per year i.e. e.g. in year 5 2.5%

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technology combined e.g. with CRT filters. Depending on the technology mixture used by truck producers to achieve the Euro 4/5 standard, fuel consumption can be slightly lower or higher compared to Euro 3 trucks.

Graph 1: Correlation Truck Age and SFC

Source: Grütter Consulting for EnAW (Swiss Private Sector Energy Agency) and BfE (Swiss Energy Office), 2004

Nationally used emissions inventories depart also in many cases from constant SFC of HDVs (Heavy Duty Vehicles) since Euro 1 i.e. since 19936.

Also the Office of Energy Efficiency of Natural Resources of Canada shows that the SFC of HDV has not improved in the last decade with values for SFC for 1997 being even slightly lower than the value for the last available year 20077.

Based on above it can be concluded that it is reasonable to assume no technology improvement factor for HDV emissions. This approach has also been followed by the approved CDM methodology

6 see e.g. for Germany “Umweltbundesamt Berlin, Handbuch Emissionsfaktoren des Strassenverkehrs, 1999, p.52 7 See: http://oee.rncan.gc.ca/corporate/statistics/neud/dpa/tablestrends2/tran_bct_40_e_4.cfm?attr=0 Based on the remark above indicating that HDVs prior Euro 1 i.e. prior 1993 having higher fuel consumption a reduction of fleet consumption values between 1990 and the 2003 could be expected due to replacement of such elder vehicles (assuming an average 10-year replacement period); Since 2000 however no further improvement would be expected; Data from NRC corroborates this statement.

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for freight mode shift AM0090 which has constant emission factors per tkm for trucks based on French studies realized8.

8.3.3. Technology Improvement Factor Mobile Equipment

No data on different sources of mobile equipment is available. Heavy equipment use technologies also prevalent in HDVs. As long as no detailed information is available thus no technology improvement factor is assumed and only a historic baseline approach is taken.

9. Project Emissions

Project emissions are calculated separately for mobile equipment and for HDVs.

Fuel policy changes e.g. the promulgation of low carbon fuel standards are included in the Emission Factor i.e. the project does not receive emission reductions for changes in fuel policies leading to reduced carbon emissions from fuels such as e.g. a compulsory blending of bio-fuels. In such cases the Emission Factor for the project as well as for the baseline is adjusted in the same ratio e.g. if a 5% blend by energy content is realized then the Baseline EF as well as the Project EF are multiplied with the factor 0.95 to account for the bio-fuel share.

Baseline emissions and project emissions are calculated the same manner. The differences are:

• The project emission factor is monitored while the baseline factor is determined ex-ante. Its calculation is however identical.

• The baseline emission factor is a historical baseline combined with a technology improvement factor. Again however the calculation procedure is identical to the project emission factor.

The only difference between baseline and project emission calculations is that for the sake of a conservative approach in the case of determination of the baseline emission factor the lower 95% confidence interval is taken and in case of the project the upper 95% confidence interval is used. This restriction applies if project emissions are based on samples and not on a full data collection. This restriction is realized to provide for conservative estimations of emission reductions and to eliminate giving credits caused by random variations.

In chapter 9.1. potential project emission sources are identified. Thereafter the procedure to calculate project emissions is determined.

8 See AM0090 Table 2 for EF per freight type based on Ministère de l’écologie, de l’énergie, du développement durable et de la mer, 2009 ; The values of this table are based on gCO2 per net tkm i.e. only freight weight is included

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9.1. Identification of Project Emission Sources

Project idem to baseline emission sources are those that are under control of the project. Leakage emission sources are those that are caused indirectly by the project activity but are not under its control. See for a discussion of leakage emissions chapter 10.

To assess the relevance for project emission sources, the following criteria were used:

• No change between project and baseline: if equivalent emission sources do not change between the project and the baseline then this source is excluded as it would have no influence on emission reductions.

• Emissions greater for baseline than project: if estimated baseline emissions are larger than estimated equivalent project emissions then this source can be excluded 9. This is a conservative approach.

The following table presents the project emission sources and a discussion of their relevance. For non-relevant parameters a justification is provided.

Table 5: Project Emission Sources Emission Source Relevance Justification P1: HDV/Mobile machinery loading and unloading

Not relevant

No change between project and baseline as project activities do not include loading or unloading

P2: HDV/Mobile machinery operation

relevant Most important emission source; This is monitored

P3: HDV/Mobile machinery maintenance

Not relevant

No change between project and baseline; equipment used does not affect HDV/Mobile machinery maintenance as latter is basically related to mileage and/or equipment age which is not influenced by the project activities.

Upstream and downstream emissions include:

• Equipment construction and decommissioning; • Upstream fuel emissions from extraction, processing and transport; • Transport infrastructure construction and maintenance;

These emission sources are not included as baseline but as leakage emission sources (see chapter 10).

9.2. Quantification of Project Emission Sources

𝑃𝐸𝑦 = 𝐹𝐶𝑥,𝑦 × 𝑁𝐶𝑉𝑥 × 𝐸𝐹𝐶𝑂2,𝑥 (3)

9 Exclusion is e.g. done due to lack of precise data availability or costs involved to collect data being higher than expected benefit from emission reductions associated to this source

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Where:

PEy Project emissions in the year y (tCO2) FCx,y Fuel consumption of project units using fuel type x in the year y (tons) NCVx Net calorific value of fuel x (MJ/t) EFCO2,x Carbon emission factor for fuel type x (tCO2/MJ) x Fuel type

In case of samples the total fuel consumption is based on

∑ ×=i

yitkmiTKMy ALSFCFC ,,, (4)

Where: FCy Fuel consumption of project units in the year y (tons) SFCTKM,i Specific fuel consumption per gross ton-kilometre of truck transporting freight type i

(tons/tkm) ALtkm,,y Activity level of project in terms of gross tkm of freight type i transported by the

project system in the year y (tkm)

Formula (4) shows the identity between project and baseline emission calculation procedure. Formula (4) is idem to formula (1) with SFC replacing EF. EF is calculated in formula (3) based on the NCV and the EFCO2. Different formulations were used as baseline data is considered a priori as a sample even if all trucks are included. This is conservative and allows for making a benchmark with a 95% confidence interval thus not issuing emission reductions credits for random variations. Project fuel consumption data however in general is available for the entire fleet. In this case formula (3) is used and no confidence interval is calculated as total project emissions have been determined based on total fuel consumption levels. If project fuel consumption data is however available only for a sample of units then formula (4) is applied which is based on the specific fuel consumption value determined based on a sample using the upper 95% confidence boundary and multiplying this project average fuel consumption factor with the activity level of the project idem to the baseline approach.

If bio-fuel blends are used the bio-fuel part is counted as 0-emission. In case of published national levels of CO2 per unit of fuel, these can be used. In case of gaseous fuels being used the EFCH4 must be included. Latter is basically expressed in terms of distance driven. Therefore in these cases the distance driven of gaseous project vehicles must also be recorded. The EFCH4 must be multiplied with the GWP of CH4 as published by the IPCC.

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10. Leakage

Leakage emission sources are those that are caused indirectly by the project activity but are not under its control.

To assess the relevance for leakage emission sources, the following criteria were used:

• No change between project and baseline: if equivalent emission sources do not change between the project and the baseline then this source is excluded as it would have no influence on emission reductions.

• Emissions greater for baseline than project: if estimated baseline emissions are larger than estimated equivalent project emissions then this source can be excluded10. This is a conservative approach.

The following table presents the leakage emission sources and a discussion of their relevance. For non-relevant parameters a justification is provided.

Table 6: Leakage Emission Sources Emission Source Relevance Justification L1: Upstream fuel emissions (extraction, refinery, transport)

Not relevant

Baseline has higher leakage emissions than project. This is due to the fact that the project uses less fuel than the baseline. Its omittance is thus conservative.

L2: Upstream equipment construction and downstream equipment decommissioning

Not relevant

No change between project and baseline as the same vehicles are used. Specific project equipment like e.g. idling-stop equipment have marginal material usage.

L3: Upstream transport infrastructure construction and maintenance

Not relevant

No change between project and baseline as the same transport infrastructure is used.

Upstream leakage from fuel usage (Well-to-Tank) is a significant emission source. However the project reduces fuel usage and therefore the upstream leakage emissions are negative i.e. the project reduces emissions beyond those accounted for. Based on a conservative accounting principle for emission reductions negative leakage emissions are not included as they are not under the direct control of the project and provoked indirectly through the project activity. This is a conservative approach in line with CDM principles to only account for leakage in case this reduces emission reductions.

Based on above considerations no leakage emission sources are included.

10 Exclusion is e.g. done due to lack of precise data availability or costs involved to collect data being higher than expected benefit from emission reductions associated to this source

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11. Quantification of Net GHG Emission Reduction

𝐸𝑅𝑦 = 𝐵𝐸𝑦 − 𝑃𝐸𝑦 (4)

Where:

ERy Emission reductions in the year y (tCO2) BEy Baseline emissions in the year y (tCO2) PEy Project emissions in the year y (tCO2)

12. Data and Parameters not Monitored

In addition to the parameters listed in the tables below, the provisions on data and parameters not monitored in the tools referred to in this methodology apply.

Data unit /Parameter: NCVx in MJ/ton

Description: Net calorific value of fuel x

Source of data: Regional or national default values

or

IPCC default values at the lower limit of the uncertainty at a 95% confidence interval as provided in Table 1.2 of Chapter 1 of Vol. 2 (Energy) of the year 2006

Justification of choice of data or description of measurement methods and procedures applied:

Any comment:

Data unit /Parameter: EFCO2,x in tCO2/MJ

Description: CO2 emission factor for fuel type x

Source of data: Regional or national default values

or

IPCC default values at the lower limit of the uncertainty at a 95% confidence interval as provided in Table 1.4 of Chapter 1 of Vol. 2 (Energy) of the year 2006

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Justification of choice of data or description of measurement methods and procedures applied:

Any comment:

Data unit /Parameter: EFCH4 in gCH4/km

Description: Methane emission factor per kilometre

Source of data: Measurements recognized by national authorities of CH4 emissions for trucks using gaseous fuels or Latest published IPCC values for HDVs as published in IPCC, 2006, table 3.2.4 of Chapter 3 of Vol. 2 (Energy) of the year 2006

Justification of choice of data or description of measurement methods and procedures applied:

Any comment:

Data unit /Parameter: EFtkm,i in gCO2/tkm

Description: Emission factor per gross ton-kilometre of HDV transporting freight type i

Source of data: Default value in methodology

or

Values as calculated in validated and registered PDs (CDM, VCS or same standard as applied for by the project activity)

or

Calculated values based on national, regional or company data not elder than 3 years

Justification of choice of data or description of measurement methods and procedures applied:

The procedures as described in chapter 8.2.1. must be followed.

Following steps must be made:

1. Determine freight/cargo type category. 2. Measurement of data over minimum 1 year of fuel

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consumption and gross tkm. 3. The sample size must be sufficiently large to ensure a

95% confidence level with a maximum 5% error boundary.

4. Measured data must be checked for homogeneity. The lower boundary of the 95% confidence interval should have a deviation of less than 10% from the average value.

5. The lower 95% confidence interval of the sample is taken using a Student’s t-test for samples of less than 50 units or a z-test for larger samples.

6. The calculation of CO2 emissions derived from fuel consumption is based on fuel consumption x NCV x EF for fuel type x (see also formulae 3) or by multiplying the fuel consumption with an official national CO2 emission factor per unit of fuel.

Any comment: See Annex 1 for an example

Data unit /Parameter: EFAI,i in gCO2/xxx

Description: Emission factor per activity indicator of equipment/machinery type i (gCO2/xxx)

Source of data: Default value in methodology

or

Values as calculated in validated and registered PDs (CDM, VCS or same standard as applied for by the project activity)

or

Calculated values based on national, regional or company data not elder than 3 years

Justification of choice of data or description of measurement methods and procedures applied:

The procedures as described in chapter 8.2.2. must be followed.

Following steps must be made:

1. Determine activity level indicator. 2. Measurement data over minimum 1 year of fuel

consumption and activity level indicator must be available.

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3. The sample size must be sufficiently large to ensure a 95% confidence level with a maximum 5% error boundary.

4. Measured data must be checked for homogeneity. The lower boundary of the 95% confidence interval should have a deviation of less than 20% from the average value.

5. The lower 95% confidence interval of the sample is taken using a Student’s t-test for samples of less than 50 units or a z-test for larger samples.

6. The calculation of CO2 emissions derived from fuel consumption is based on fuel consumption x NCV x EF for fuel type x (see also formulae 3) or by multiplying the fuel consumption with an official national CO2 emission factor per unit of fuel.

Any comment: Indicators can be related to the equipment/machinery itself or to the production output e.g. amount of processed material. They should however not be related to the monetary value of output as latter is dependent on market prices and thus not linked directly to energy usage.

13. Monitoring Description

All data collected as part of monitoring should be archived electronically and be kept at least for 2 years after the end of the last crediting period. 100% of the data should be monitored if not indicated otherwise in the tables below. All measurements should be conducted with calibrated measurement equipment according to relevant industry standards.

14. Data and Parameters Monitored

In addition to the parameters listed in the tables below, the provisions on data and parameters monitored in the tools referred to in this methodology apply.

Data Unit/Parameter: FCx,y in tons (or converted to tons from liters or m3)

Description: Fuel consumption of project units using fuel type x in the year y

Source of data: Truck / equipment operator

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Description of measurement methods and procedures applied:

Data can be based on:

Total fuel consumption for all units based on property fuel stations or on records including invoices from fuels stations used by project equipment / trucks.

Or

RFID or other form of data measurement at vehicle/equipment level.

Or (only if non of above is available)

Sample measurement per freight type i for trucks and activity indicator type i for equipment/machinery together with activity levels. The sample must comply with following conditions:

1. Measurement data over minimum 1 year of fuel consumption and activity level indicator must be available (to ensure non-seasonality of data).

2. The sample size must be sufficiently large to ensure a 95% confidence level with a maximum 5% error boundary.

3. The upper 95% confidence interval of the sample is taken using a Student’s t-test for samples of less than 50 units or a z-test for larger samples. This ensures that the project fuel consumption taken is conservative.

Frequency of monitoring/recording:

Continuous with minimum annual reporting in the case of total fuel consumption. Annual in case of sample with sample distribution over entire year.

QA/QC procedures to be applied: Fuel consumption data is cross-checked with invoices.

In case of samples the upper 95% confidence interval is taken.

Any comment:

Data Unit/Parameter: ALtkm/AI,y in gross tkm or to be determined by chosen activity indicator

Description: Activity level of project in terms of gross tkm of freight type i or activity indicator of equipment/machinery type i by the project system in the year y

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Source of data: Truck / equipment operator

Description of measurement methods and procedures applied:

Total gross tkm (or activity level indicator) for all units.

Gross tkm has to be measured in identical manner (same option) as for baseline measurements referring basically to:

• Preferred option: tkm is determined per trip with gross tonnage of truck per trip x distance driven of this trip

• 2nd best option: average gross weight over time period x total distance driven of all trucks over same time period

For measurement of gross weight the following options exist:

• Preferred option for Gross weight is based on measurement and recording of weight truck per trip (net freight weight plus tare weight of truck configuration used) e.g. based on weighting of freight or on automatic truck weighting equipment

• 2nd best option: a standard net weight is assumed per truck based on recorded and provable company experience

• 3rd best option: gross weight is assumed as maximum permissible gross vehicle weight rating (GVWR) for which the vehicle is allowed to operate. GVWR in technical terms might be higher than in permissible legal terms in the specific country/region as vehicles might be technically certified for higher weights than allowed in a specific country/region e.g. due to local regulations including maximum allowable load on bridges. In such cases always the legal maximum allowable weight is taken even if this is lower than the technical GVWR.

• In fleets vehicle weights might be available for some trucks and for others not. A comparable relation of options 1-3 should be used in the baseline as well as during project monitoring.

Distance driven can be based on GPS, RFID or company records based e.g. on odometer, route maps and number of turn-arounds. The method to determine distance driven must be described in the PD.

For activity based indicators on time e.g. operating hours for

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machinery latter should be based on hour-monitoring devices installed at the machinery and electronic or paper records. The indicator for equipment/machinery and its measurement must be detailed in the PD.

Activity indicators cannot be changed between baseline and project monitoring periods and must be measured in comparable manners.

Frequency of monitoring/recording:

Continuous with minimum annual reporting.

QA/QC procedures to be applied: Total distance driven per truck unit is calculated and compared with previous years to check plausibility of information.

For indicators based on time measurements total machine hours per annum are calculated and compared with previous years.

Any comment:

15. References and Other Information

• CO-OP, Data on fuel consumption and tkm for trucks year 2009, 2010 (calculations performed by Grütter Consulting)

• Environment Canada, GHG Emissions Quantification Guidelines, 2010 • IPCC, Guidelines for National Greenhouse Gas Inventories, 2006

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Annex 1: Default Baseline Emission Factor Forestry

Emission Factor: Forestry industry incl. log transport and associated with trip driving ranges > 15 km per trip

Unit: gCO2/gross tkm

Definitions:

tkm ton-kilometer based on tons multiplied with distance driven per trip

gross tkm gross weight i.e. vehicle empty weight plus freight. In case the freight weight is not known the project proprietor can use the maximum allowed vehicle weight as default value or a calculation method to be approved by the validator. The same method should be used for the same truck or fleet during the project crediting period for consistency purposes.

Data for the benchmark is based on daily records of trucks for tkm recorded as exemplified in the following table.

Table A1: Data Collection Gross tkm of Forestry Trucks Date Truck ID Kms Gross weight Route Gross tkm11

05/01/2009 203 37.0 32.56 Lakeland-PBEC 1,205.05 05/01/2009 203 37.0 41.50 Lakeland-PBEC 1,535.92 05/01/2009 203 37.0 41.65 Lakeland-PBEC 1,541.47 05/01/2009 203 37.0 41.65 Lakeland-PBEC 1,541.47 06/01/2009 203 37.0 35.86 Lakeland-PBEC 1,327.18 06/01/2009 203 26.6 36.50 PGSawmill-Brink-TB-SU 970.90

Source: COAC, 2011

For each individual truck this data was collected for an entire year. Daily data on tkm was thus available.

Data per truck was then aggregated for 1 year. Fuel consumption of this truck was also available for 1 year. To relate fuel consumption to each trip is technically only possible if fuel consumption is recorded electronically on board and separately registered for each trip. Even in such cases thus does not make that much sense as basically aggregate data give an indication of average fuel consumption over a period of time, with different road, traffic and weather conditions as well as with different drivers.

Table A 2 shows the aggregated data for 42 trucks.

11 Kms multiplied with gross weight

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Table A2: Annual Data of Forestry Trucks Truck ID Gross tkm Fuel used in liter l/tkm gCO2/tkm

203 3,247,983 43,817 0.013 36 207 2,465,953.11 36,649.80 0.015 40

211PG 5,717,689.35 64,372.90 0.011 30 217 5,719,443.76 68,111.50 0.012 32 218 5,939,544.94 71,214.30 0.012 32 219 5,653,824.96 65,114.20 0.012 31 220 3,454,090.82 44,933.80 0.013 35 221 6,505,149.53 69,665.50 0.011 29 222 2,142,064.86 31,151.50 0.015 39 223 7,303,824.69 74,933.80 0.010 27 224 4,680,123.87 68,081.20 0.015 39 226 4,095,973.52 51,306.50 0.013 33 230 4,254,871.04 55,027.30 0.013 34 231 3,527,119.44 39,337.60 0.011 30 232 4,540,753.59 61,476.30 0.014 36 236 3,083,866.02 42,920.60 0.014 37 239 6,522,876.97 78,615.20 0.012 32 240 7,763,475.14 83,395.20 0.011 29 241 8,477,040.87 94,844.50 0.011 30 242 7,445,164.32 78,561.90 0.011 28 243 7,917,040.67 97,666.90 0.012 33 244 5,197,314.26 70,070.00 0.013 36 245 8,233,058.14 95,395.70 0.012 31 287 2,148,882.83 26,793.80 0.012 33 289 3,348,339.71 45,072.00 0.013 36

290PG 3,348,891.81 46,723.00 0.014 37 297PG 3,144,241.37 41,462.80 0.013 35

299 3,804,271.32 49,061.80 0.013 34 302 9,658,544.79 103,655.50 0.011 29 303 10,360,569.58 116,993.40 0.011 30 304 9,786,240.86 105,063.90 0.011 29 305 7,305,880.26 83,505.20 0.011 30 308 10,119,911.86 109,784.50 0.011 29 309 9,682,614.93 110,881.50 0.011 30 310 6,157,015.54 72,335.10 0.012 31 311 9,357,415.81 63,546.60 0.007 18 316 9,569,425.01 107,641.97 0.011 30 317 9,548,421.89 108,184.90 0.011 30 318 9,985,378.83 109,565.10 0.011 29 319 7,588,069.81 87,609.70 0.012 31 322 6,996,479.26 72,395.33 0.010 28

5523 7,369,672.65 82,115.04 0.011 30 Source: COAC, 2011

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The conversion from fuel per liter to gCO2 is based on the Emission Factor for diesel trucks of 2,663 gCO2 per liter diesel as published by Environment Canada (see http://www.ec.gc.ca/ges-ghg/default.asp?lang=En&n=AC2B7641-1).

Table A 3 shows the statistical results of this data.

Table A3: Results Forestry Trucks Item Result Sample size 42 average gCO2/tkm 31.8 standard deviation SD 4.0 standard error of average σ 0.6 t-test with 95% confidence lower boundary d.f. n-1 gCO2/tkm 30.6

The sufficiency of the sample size is based on following test:

Minimum Sample Size = (Standard Deviation * 1.96 / (average value * maximum error bound)) ^ 2

The value of 1.96 is based on the z-distribution for a 95% confidence interval The maximum error bound is 0.05 The SD (Standard Deviation) is based on the sample

Based on this formula the required sample size is 24 units while the actual sample size is 42 units and thus sufficient to ensure a 95% confidence level.

The benchmark/default factor is the lower 95% confidence interval of the value found using the t-test. This is a conservative approach as baseline emissions are thus within a 95% confidence interval equal to or higher than the default value used.

The resultant value is therefore rounded 31 gCO2/gross tkm.

Table A4 shows the results of trucks with driving distances per trip below 15km. Obviously the emission factors for these trucks is far higher due to basically being used for stop and go trips within compounds at low speed and very short distances. This group of trucks/trips can thus not be included in the same group as trucks riding much longer distances.

Table A4: Annual Data of Forestry Trucks with Short Trip Distances Truck ID Gross tkm Fuel used in liter l/tkm gCO2/tkm 206PG 1,065,680.66 26,246.30 0.025 66

208 694,694.81 20,320.20 0.029 78 209 1,258,617.25 29,337.40 0.023 62 212 1,574,468.10 29,955.90 0.019 51 298 1,321,077.97 29,851.40 0.023 60 323 2,121,723.51 41,311.00 0.019 52

Source: COAC, 2011