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 GHGenius 4.03 Manual Volume 2
 Prepared March 2013
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The GHGenius user manual has been updated to version 4.03 of the model. The manual is split into two volumes. Both volumes are organized by sheet in the model.

Volume 1 of the manual documents the structure of the model, the important parameters, and the similarities and differences with the LEM model.

Volume 2 of the manual documents the data and data sources that are used in the model.

The manual is a work in progress. It is the intention to continually update the manual as new versions of the model are released.


Tags: GHGenius 4.03 - Manual
 GHGenius Manual 4.03 Volume 1
 Prepared March 2013
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The GHGenius user manual has been updated to version 4.03 of the model. The manual is split into two volumes. Both volumes are organized by sheet in the model.

Volume 1 of the manual documents the structure of the model, the important parameters, and the similarities and differences with the LEM model.

Volume 2 of the manual documents the data and data sources that are used in the model.

The manual is a work in progress. It is the intention to continually update the manual as new versions of the model are released.


Tags: GHGenius 4.03 - Manual
 Palm Oil and Biofuel Update
 Prepared March 2013
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The palm oil pathway was added to GHGenius in 2006. It had not been reviewed or updated since that time. There is considerably more information now available on the production system since the EPA spent several years studying the pathway for the RFS2 program and the industry in Malaysia and Indonesia released a lot of information in response to the preliminary EPA findings. Some palm oil based biofuels are being used in Canada, so it is appropriate to review and update the pathway. Particular attention was paid to the modelling of emissions from the soils in general and the peat soils in particular.

Another biofuel feedstock that is starting to be produced in Canada and is being used in the United States is corn oil extracted from the stillage of ethanol plants. This product is already a co-product of the ethanol production process, so it was relatively straightforward to add it as a feedstock for biodiesel and HRD production in the model.

The Canola Council of Canada and Agriculture and AgriFood Canada (AAFC) undertook a survey of 1000 canola producers in 2011. This survey has resulted in a wealth of information concerning fertilizer application rates, fuel usage, and pesticide application rates. This data was used to develop GHG emission data for canola production in Canada at the eco-zone level. GHGenius has been updated with this data.

AAFC also made available information on soil carbon changes by soil zone and province for the work for the Canola Council. The same information is available for all provinces with agricultural area. The US national GHG inventory reports have also been reviewed to extract the soil carbon changes due to land management change in the US. This work updated both the US and Canadian soil carbon data in the model.

The work also updated some of the N2O emission calculations with several pieces of new data. AAFC supplied the leaching emission factor by province, which was used in the model to develop regional values. There is also an AAFC paper on the ratios of grain to biomass and the nitrogen contents of above and below biomass. This data was reviewed and compared to the IPCC recommended values. The data in the model for different feedstocks comes from several sources so it would be advantageous to use one data source for most feedstocks.

The chemicals used in the biodiesel manufacturing process are based on an NBB survey undertaken in 2009, but it is believed that this data was misinterpreted when it was first produced and it reports the usage of diluted solutions for catalyst and hydrochloric acid and not the actual usage of those chemicals. This has been corrected in this version of the model.

Finally, we have reviewed the data available from Statistics Canada and AAFC on manure application rates in Canada. It would appear that there is information available that would allow a more precise estimate of manure use for fertilizer in Canada.


Tags: Canola - Corn Oil - GHGenius 4.03 - Land Use - Palm Oil
 Crude Oil Production Update 2013
 Prepared March 2013
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A significant number of changes have been made to the crude oil production calculations in GHGenius. More individual countries have been added, additional sources of information on the energy use and GHG emissions in producing countries have been reviewed, and some enhancements in the calculation of GHG emissions from gas flaring has been made.

The emissions from the production of crude oil, particularly crude oil produced outside of Canada, remains a subject of significant interest. New information has become available from work funded by Alberta Energy that looked at crude oils that are supplied to European refineries, the California Air Resources Board have released their OPGEE model and estimated carbon intensities of crude oils refined in California, and other publications on the subject have been produced.

One of the shortcomings in the current version of GHGenius is the degree of aggregation for foreign crude oils. Ten countries supply more than 75% of the imported crude oil but only 13% of the imports are from a specific country in the model and the rest are combined into a region. Some of these regions are likely to have quite different production characteristics, Russia and Norway for example. We have added seven more countries to the model so that imports from the top ten countries can be modelled as countries rather than regions.

The structure of the model has required the same changes to be made to the US regions, India and Mexico. At the same time import data up to the calendar year 2011 has been added to the model. Crude oil quality and production emissions have been reviewed and updated for the new countries as well as the regions that are not being changed. The recent Alberta Energy work, the OPGEE results, the annual OGP data, and any new sources that can be found have been reviewed for use in the model.

All of the crude oil transportation distances have been reviewed as we added the new countries to the model. We have paid particular attention to transportation distances in the country of origin prior to reaching the shipping port. We have also reviewed and added power production for oil production to each of the new countries.

A large number of changes were made to the model but the changes with the most impact on the emissions were related to venting, flaring, and fugitive emissions. These are calculated more rigorously in the model now. For most regions of the world this has resulted in an increase in methane released for crude oil production and this increases the lifecycle GHG emissions. We have updated the emissions from flaring and venting associated with crude oil production. The World Bank flaring project has released updated data for 2010 that will be reviewed for the model. Flaring emissions will be done in a more rigorous manner so that N2O emissions are also included, not just the methane and CO2 emissions.

There were very few changes made to the oil production data for Canada. Just a reduction in energy use in the offshore region and the changes in the calculation of flaring emissions, where the volume for Canada did not change but the emission calculation did.


Tags: Crude Oil - GHGenius 4.03
 Fuel Economy Update
 Prepared March 2013
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The fuel economy of the base vehicles in GHGenius has been almost constant, with only a very small change between 1995 and 2025 and then a slightly larger change between 2026 and 2050. The rates of change that were in the model were estimates made in conjunction with NRCan staff almost 15 years ago. There is new data from the EPA and Transport Canada that can be used to get much better estimates of the rate of change in fuel consumption in both Canada and the US for use in the model.

There have also been new regulations introduced that will see fuel consumption of new vehicles in Canada and the United States drop dramatically, first between 2012 and 2016 and then in a second step between 2017 and 2025. This new regulatory requirement has been incorporated into the model.

The fuel economy in the model is also used to set the vehicle weight and the distribution of materials used in the construction of the vehicle. This data is then used to calculate the GHG emissions from manufacturing and assembling the vehicle. One set of data that was developed for the Transportation Table in 1999 has been used in the model.

This work has reviewed the weight data and adjusted the vehicle weight - fuel economy relationship in the model. This has given a much better representation of the vehicle manufacturing emissions. We have developed historical relationships and use that information along with information from the EPA that was used to develop the new standards to arrive at a forecast for future changes.

The work has also reviewed the literature for any new information on the distribution of different materials in different classes of vehicles.


Tags: Fuel Economy - GHGenius 4.03
 2013 US Update
 Prepared March 2013
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The update of the core US data in the model has resulted in some changes in some of the pathways. As expected, the carbon intensity of electric power has been reduced due to the competitiveness of natural gas. There was reduced coal use for electric power and increased gas consumption. Hydropower also increased in the US West region, the only region with significant hydropower, although this could be due to annual weather patterns.

Overall there is little change in the GHG emissions for petroleum products. Increased emissions for crude oil production have been offset by reductions in the refinery. CNG for light and heavy-duty vehicle use have slightly larger GHG emission reductions compared to gasoline and diesel fuel in this version of GHGenius compared to version 4.02. Natural gas production energy use is lower in the latest set of data from the US EIA.

Natural gas as a transportation fuel is gaining attention in the US and in Canada. It was shown earlier that there are some reductions in the natural gas emissions delivered to an industrial user as a result of the data update. These upstream emissions should also be apparent in the natural gas for vehicle pathways, along with any changes in the electric power carbon intensity.

The update of the energy data has a small impact on the emissions for corn ethanol and soybean biodiesel, as natural gas and electricity have lower carbon intensities as a result of this update.


Tags: Crude Oil - Electricity - GHGenius 4.03 - Natural Gas - United States
 Hydroelectricity Emission Review
 Prepared March 2013
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Hydroelectricity provides about 60% of Canada’s electricity. It is known that there is some methane that is generated from decomposing biomass in the flooded reservoirs behind the dams. The estimate of the rate of methane production currently in GHGenius is from a single reference in the original Delucchi LEM model.

Recently there has been significant work undertaken in this area. There are some estimates in the National GHG Inventory report but the academic literature has been quite active in this area. This report summarizes the findings from a literature review of the subject.

There is a wide range of GHG emission estimates for hydroelectric reservoirs in the literature. There are several common themes in many of the papers, reports, and regulatory filings:
1. It would appear that there is some consensus that emissions are higher in tropical climates than temperate or boreal climates.
2. Measured real world emissions appear to be higher than the emission estimates from the forecast in the Environmental Impact Statements for projects.
3. The emissions of both carbon dioxide and to a lesser degree methane vary with time. Emissions are highest immediately after flooding and decay over time. The documented rates of reduction vary significantly.
4. The quantity and quality of the reports studying these emissions is increasing with time. Some of the newest, most detailed reports are finding emissions much higher than earlier studies have found.


Tags: GHGenius 4.03 - Hydroelectricity
 GHGenius 4.02 Manual Vol 2
 Prepared October 2012
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The GHGenius user manual has been updated to version 4.02 of the model. The manual is split into two volumes. Both volumes are organized by sheet in the model.

Volume 1 of the manual documents the structure of the model, the important parameters, and the similarities and differences with the LEM model.

Volume 2 of the manual documents the data and data sources that are used in the model.

The manual is a work in progress. It is the intention to continually update the manual as new versions of the model are released.


Tags: GHGenius 4.02 - Manual
 GHGenius 4.02 Manual Vol 1
 Prepared October 2012
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The GHGenius user manual has been updated to version 4.02 of the model. The manual is split into two volumes. Both volumes are organized by sheet in the model.

Volume 1 of the manual documents the structure of the model, the important parameters, and the similarities and differences with the LEM model.

Volume 2 of the manual documents the data and data sources that are used in the model.

The manual is a work in progress. It is the intention to continually update the manual as new versions of the model are released.


Tags: GHGenius 4.02 - Manual
 Advanced Biofuel Update
 Prepared October 2012
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The US National Renewable Energy Laboratory has published a number of techno-economic assessments over the past several years on advanced biofuel pathways. These studies include:
· Ethanol via biochemical processes using agricultural residues.
· Ethanol via thermochemical process using woody biomass.
· Gasoline via thermochemical process using woody biomass.
· FT Distillate via thermochemical process using woody biomass.
· Gasoline and Diesel fuel via pyrolysis.


Additional information has been found for the wood to DME process and this information is used to update this pathway in GHGenius. All of these pathways are already in GHGenius, having been added over the years. This work updates the default values in the GHGenius model and uses the updated data to model and report on the energy balances and GHG emissions of the pathways. This includes undertaking sensitivity analysis on the important variables.

In GHGenius there are multiple sub-pathways for some of these fuel systems, for example there are four agricultural feedstocks and a wood feedstock for the biochemical route and the pyrolysis pathway has woody biomass feedstock and agricultural residues. We have developed consistent inputs for all of these sub-pathways based on the NREL data, whereas they only provide data for one feedstock.

There are process developers that have hybrid systems, those that use both biochemical and thermochemical process to convert biomass to transportation fuels. These can also be modelled in GHGenius by adapting one of the existing pathways. Two examples of this are presented, but the inputs required for these systems are not included as default values in the model.

The results are presented for Canada for the year 2012 using the IPCC 2007 GWPs, unless otherwise stated. The version of GHGenius that accompanies this report is version 4.02. This version also has some updates to the electric and fuel cell vehicle pathways, which are described in a separate report. The GHGenius manuals have also been updated with the information used in this report.


Tags: DME - Ethanol - Feedstock - Fischer Tropsch - GHGenius 4.02 - PyrolysisOils - Wheat Straw - Wood
 EV Update
 Prepared October 2012
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There is increased interest in electric vehicle mobility with the commercial availability of electric vehicles from several manufacturers and the increased visibility of fuel cell vehicles in large demonstration projects in Canada and elsewhere in the world.

The functioning of the EV section of the model had been reviewed and, while it was functioning correctly, there were a number of key input values and assumptions that have a large influence on the model results. Many of these date back to the original LEM model. With the new electric vehicles on the market, it has been possible to update many of these parameters and algorithms with values that are representative of the current EV models. Two of the key issues investigated were the factors for changes in vehicle weight as a function of battery weight, and the changing efficiency of the vehicle as total weight changes.

Data has been found for the relative performance of EV vehicles for the new vehicles that have been tested by the US EPA. Other issues investigated include the vehicle range, the charging efficiency, the battery efficiency, battery specific energy, battery cycle life, and depth of discharge. The model has maximum and minimum default values for all of the parameters and they have been evaluated to ensure that they are realistic.

The plug-in electric vehicle pathway was added to GHGenius in 2006. There is much more known about these vehicles now that some are commercially available. The latest information on these systems has been reviewed to determine if any changes are required to the structure of GHGenius to better model these vehicles.

Since electricity is the focus of this work, the electric power forecasts in the model have been updated from the 2009 to 2011 NEB forecast. At the same time, the structure of the model has been changed from new forecast values every five years to a value for every year of the forecast period.

There is finally some public information on the performance of FCV that has been reviewed and used to improve the model. The B Class Mercedes is available in both gasoline and FCV variations and test data is available on the Honda Clarity FCV, although there isn’t an exact match gasoline vehicle.

Tags: Electric Vehicles - Electricity - Fuel Cell Vehicles - GHGenius 4.02 - PHEV
 GHGenius Manual 4.01 Vol 2
 Prepared May 2012
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The GHGenius user manual has been updated to version 4.01 of the model. The manual is split into two volumes. Both volumes are organized by sheet in the model.

Volume 1 of the manual documents the structure of the model, the important parameters, and the similarities and differences with the LEM model.

Volume 2 of the manual documents the data and data sources that are used in the model.

The manual is a work in progress. It is the intention to continually update the manual as new versions of the model are released.


Tags: GHGenius 4.01 - Manual
 GHGenius Manual 4.01 Vol 1
 Prepared May 2012
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The GHGenius user manual has been updated to version 4.01 of the model. The manual is split into two volumes. Both volumes are organized by sheet in the model.

Volume 1 of the manual documents the structure of the model, the important parameters, and the similarities and differences with the LEM model.

Volume 2 of the manual documents the data and data sources that are used in the model.

The manual is a work in progress. It is the intention to continually update the manual as new versions of the model are released.


Tags: GHGenius 4.01 - Manual
 Natural Gas Update Report
 Prepared May 2012
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This report documents changes made to the complete natural gas lifecycle emissions. It includes all of the information that is also included in the NG Downstream Report. The tasks that this report covers includes:

Task 1: Update Conventional Natural Gas Production and Energy Data

The existing data for conventional natural gas in the GHGenius model has been updated. This included removing the relative factors used to convert U.S. gas production energy requirements to Canadian emissions and replacing them with actual Canadian conventional gas production energy and emissions data. This results in having separate Canadian and U.S. data sets allowing for more robustness.
The natural gas processing emissions have also been updated. This includes a more robust treatment of the emissions of CO2 from the raw gas so that a proper sensitivity analysis of this parameter can be undertaken.

Task 2: Update Unconventional Natural Gas Production and Energy Data

We have updated, where possible, the existing data for unconventional natural gas pathways, including coalbed methane and tight gas, but excluded shale, as one of the recent projects covered this pathway specifically. The shale gas information has been included in the report and the model so that all of the documentation is together in one document.

Task 3: Lifecycle Analysis of Conventional and Unconventional Natural Gas Pathways

Based on the new data provided to the model, we have used GHGenius to obtain carbon intensity results for Canadian conventional and unconventional natural gas pathways, including shale, coalbed methane, and tight gas. The carbon intensity results illustrate the intensities for each of the following processing stages:
1) Natural Gas Production: This stage includes emissions from the extraction and processing of natural gas. More specifically, this stage includes emissions resulting from well exploration, well drilling, well completion, and acid gas removal.
2) Transportation: This stage includes emissions to compress the NG to transport it through the main pipeline network.
3) Distribution and storage: Includes emissions for distributing the NG from the main transmission pipelines to end users.
4) End Use
Emissions due to gas leaks and flares are captured under a separate category in GHGenius (called gas leaks and flares). This includes methane losses. In the report, these emissions are appropriately disaggregated to the life cycle stages listed above so that one can observe which stage contributes most to leaks and flares.
The functional unit is a GJ of feedstock delivered to the end user. IPCC 2007 global warming factors for a 100-year time horizon have been used. Different Canadian regions have been studied.

Task 4: Sensitivity Analysis

We have performed a sensitivity analysis on unconventional natural gas parameters such as CO2 content in formation gas, overall methane leakage from the system, pipeline distances and investigated the impact of gas production rate, venting and flaring rates at the well.


Tags: GHGenius 4.01 - Natural Gas
 Downstream Natural Gas Update
 Prepared May 2012
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GHGenius has traditionally been focussed on the emissions from the transportation sector but the model is capable of reporting emissions for all sectors of the economy. One aspect of this work was to make it easier for the model to be used for modelling emissions in these other sectors. The scope of this work included the following:

1. An update of natural gas pipeline emissions including both transmission and distribution, including their fugitive emissions. Some data was supplied by the CGA as part of this work and other data sources were also sourced. The transmission energy use and emissions have also been regionalized as part of this work.

2. A re-organizing of the GHGenius tool to model lifecycle emissions from using natural gas in a host of applications including vehicles, power generation, home heating, and industrial applications and to compare these emissions to other fuels. The model update includes a new user interface sheet that will allow the user to more intuitively select the fuel pathways they wish to compare, and it will allow for easy comparison of selected fuels and end uses. The rest of the GHGenius model operates separately from this sheet.

3. The new user selection sheet allows the user to select different fuels, the province (or region) in which the fuels are used, and the end use of the fuel. More advanced parameters will still be able to be changed elsewhere in the model. On the same sheet, a simplified set of results will be output. They will include, as appropriate, gCO2e/km, gCO2e/tonne steam, gCO2e/kWr, and percent comparisons.

In addition some changes have been made to the model to provide additional flexibility and allow more representative regionalization of the model. At the same time as this work was undertaken, the complete natural gas pathway in the model (including the gas production and processing stages) was comprehensively evaluated and changes made to better reflect the current data that is available. These changes are documented in a separate report. Data updates have been made to both the Canadian and US data in the model.


Tags: Electricity - GHGenius 4.01 - Industrial - Natural Gas - Residential
 Sugar Cane Ethanol Update
 Prepared May 2012
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This pathway in GHGenius was added to the model in 2006. Sugarcane production systems in Brazil have undergone significant change since that time with the introduction of mechanical cane harvesting, the continued evolution of the sugar and ethanol mills, and the development of a more significant co-generation capacity in the industry as a result of changing government policies.

There is also much more data available on the industry as a result of a significant effort by the Brazilian industry to document their activities and the inclusion of this pathway in the US RFS2 program and in the California Low Carbon Fuel Standard program. There is information available for both manual and mechanical harvesting. This update includes the option to allow the user to choose which harvesting approach to use for modelling. The user can specify how much of the feedstock is harvested manually and how much is done mechanically and the button installs the appropriate defaults throughout the model.

Where possible, the data in the model has been converted to the time series approach that has been used in recent updates to the model. This provides greater certainty for the input data as it effectively deals with year to year variations due to weather.

Particular attention has been paid to soil emissions and fugitive emissions from the vinasse disposal systems. These two issues were identified as areas of uncertainty and ongoing research in Brazil.

The version of the model that accompanies this report is GHGenius 4.01. Several other changes to the model were carried out at the same time but these have their own reports. The sugarcane ethanol system is shown in the following figure.

Tags: Ethanol - GHGenius 4.01 - Sugar Cane
 BioJet and Camelina
 Prepared May 2012
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This work had several components in it.

Reference pathways for fossil jet fuels have been added to the model. Two pathways are included, one a typical current aviation fuel with sulphur contents in the range of 500 to 1,000 ppm, and the second an ultra low sulphur with levels below 10 ppm. The low sulphur fuel may be required in the future and it may be a better comparison for the BioJet that typically has low or no sulphur. The focus of the work will be on the upstream emissions.

The processing energy requirements (and thus the GHG emissions) for BioJet processes appear to be slightly more severe than the requirements for HRD processes. Therefore an HRJ (hydrotreated renewable jet) fuel pathway has been added to the model. Similarly to the HRD pathway one pathway is depicted in the model and the user will choose the feedstock to be used.

Camelina as a biodiesel, HRD, and HRJ feedstock has been added to the model. The interest in Camelina as a biofuel feedstock is increasing. There are a number of producers in Canada interested in this feedstock.

A list of other oilseeds that are not in GHGenius that have been discussed as potential biodiesel feedstocks has been prepared. For each of these a short description of the feedstock, its benefits, and drawbacks is provided.
We have investigated the state of knowledge of the emissions from the use of aviation fuels.

We have also added the potential to use BTL fuels in the aviation sector. These fuels were already in the model for road use, so they have been included as a third option in the aviation fuel use output tables in the model.


Tags: Bio Jet - Biodiesel - Camelina - GHGenius 4.01 - HRD - Jet Fuel
 Shale Gas Update
 May 2012
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The interest in the GHG emissions of shale gas has continued to increase as the potential of the resource becomes better understood across North America. Previously, it was found that very little information was available concerning the emissions of the well drilling, “hydraulic fracturing”, and production stages of a shale gas well.

New information has been supplied to Natural Resources Canada by the Canadian Association of Petroleum Producers (CAPP) on the energy and materials consumed during the well drilling, hydraulic fracturing, and production stages. Additional information has recently been released by the US EPA, although that was focussed on updating their National Inventory Report and not on LCA work.

This work has reviewed the data supplied, compared the results between companies and reached some conclusions with respect to the data that has been made available.

To properly model shale gas the structure of the natural gas sheet in GHGenius needed to be changed to have more emphasis on the well drilling emissions. This new structure has been developed and integrated into the model.

The revised model has been used to compare the GHG emissions for shale gas with other types of gas in the model.


Tags: GHGenius 4.00 - Shale Gas
 Shale Gas Update - French
 May 2012
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L'intérêt envers les émissions de gaz à effet de serre (GES) du gaz de schiste ne cesse de croître à mesure que l'on comprend mieux le potentiel de cette ressource partout en Amérique du Nord. Un rapport précédent faisait ressortir le très peu d'information disponible sur les émissions liées aux étapes de forage, de fracturation hydraulique et de production d'un puits de gaz de schiste.

L'Association canadienne des producteurs pétroliers (ACPP) a fourni à Ressources naturelles Canada de l'information récente sur l'énergie et les matières consommées durant les étapes de forage du puits, de fracturation hydraulique et de production. En outre, l'Environmental Protection Agency (EPA) des États-Unis a récemment publié des données additionnelles, bien qu'il s'agisse plutôt d'une mise à jour de leur National Inventory Report et non d'une analyse du cycle de vie.

Pour le présent travail, nous avons effectué une revue des données fournies et une comparaison des résultats entre les entreprises, et formulé certaines conclusions en ce qui a trait aux données rendues publiques.

Pour modéliser adéquatement le gaz de schiste, il fallait modifier la structure de la feuille de calcul du gaz naturel dans GHGenius afin de mieux prendre en compte les émissions associées au forage des puits. Cette nouvelle structure a été développée et intégrée dans le modèle.

Le modèle révisé a été utilisé pour comparer les émissions de GES associées au gaz de schiste avec celles des autres types de gaz dans le modèle.


Tags: GHGenius 4.00 - Shale Gas
 Co-Products Report
 October 2011
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There were three primary tasks undertaken as part of this work. The first task was to expand the number of process chemicals that can be modelled as inputs to the various fuel production processes on the Alt Fuel Prod sheet. Recent modelling work for biodiesel plants has found that there is a significant variation in the process chemicals that are used. In addition to sodium hydroxide, some plants use potassium hydroxide or sodium methylate as the catalysts. Different acids are also used in the process. The following additional chemicals have been added to the model:
· Acetic acid
· Ammonium sulphate
· Citric acid
· Hydrochloric acid
· Magnesium silicate
· Nitric acid
· Phosphoric acid
· Potassium hydroxide
· Sodium methylate
· Sugar

One additional change to this section of the model has been made. In some processes there is a significant amount of electricity that is used for some of the chemical inputs and the model had used generic power for the calculation of emissions for these chemicals. This meant that the chemical GHG emissions were dependent on the location where they were used and not necessarily where they were produced. This has been changed so that the user can choose between using the average Canada power, the average US power or the regional generic power for the chemical production.

The second task has been to expand the treatment of co-products in the model. Over the years, the co-products have been added on a when required basis for each new pathway. This has resulted in a somewhat inconsistent treatment of the calculation methodology. A more structured approach to calculating the energy and emissions that are displaced by the co-products has been adopted, more like the approach used on the Alt Fuel Prod sheet for the input chemicals. This should make it easier to add new co-products in the future as bio-refineries develop.

Some additional co-products, or co-product uses have been added. Some biodiesel plants are using glycerine as an animal feed, or as a source of fuel. Others are using it as a feedstock for propylene glycol production.

Some LCA models use alternate co-product allocation methods other than system expansion/displacement such as mass or energy allocation. The functionality of GHGenius has been expanded to allow the user to choose between displacement (default), mass, or energy allocation.

The third part of the update has been to re-organize some sheets. As the model developed and new pathways or functions were added, the layout of some of the sheets becomes compromised and less than ideal. Many of these compromises have been addressed.


Tags: Co-products - GHGenius 4.00
 Biofuels Sustainability Report
 March 2011
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A number of jurisdictions internationally have and/or are advancing policies in the area of renewable fuels (biofuels). The United States, European Commission, and a number of European member states, amongst others, have incorporated sustainability requirements and/or minimum greenhouse gas (GHG) reduction thresholds as part of their biofuels policies. In addition, a number of international organizations, nongovernmental organizations and multi-stakeholder initiatives have or are developing recommendations based on various aspects of biofuels sustainability. These present opportunities as well as challenges for Canada’s biofuel industry and biofuel feedstocks industries.

The economic stakes for Canada’s agriculture and agri-products sector (supplying corn, canola, oil and other basic feedstock) and for bioethanol and biodiesel fuel producers may be substantial as export markets for “certified renewable” feedstocks and biofuel products grow. At the same time, market-pricing mechanisms may change where premiums become available for “certified” materials/biofuels and discounts result for non-certified materials/biofuels.

It is also highly likely that the impact of biofuels sustainability measures will impact the traditional agriculture and agrifood markets and there is some evidence that this has already started to happen. These impacts could be positive or negative and Canadian stakeholders need to be aware of the potential consequences of these activities even if no biofuel or feedstock destined for biofuel producers ever leaves Canada.

The biofuels value-adding chain or business system encompasses a number of stages that involve a variety of economic stakeholders, including but not limited to growers, fertilizer producers, transportation providers, vegetable oil producers and others that can be unique for different biofuels as well as feedstocks. There are also competing industry stakeholders (e.g., oil & gas, petroleum refining) and as a result Canadian government interests involving a number of departments (e.g., Agriculture and Agri-Foods Canada, Natural Resources Canada (NRCan), Environment Canada, Department of Foreign Affairs and International Trade). International sustainability initiatives (ISIs) bring together factors influencing these entities as well as other interests (e.g., labour interests, other social interests). Therefore, analysis of the implications of ISIs for the Canadian economy requires a broad, multidisciplinary approach.


Tags: Sustainability
 Oil Refining Update
 April 2011
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The GHG emissions related to the production and use of petroleum fuels are very important, as these fuels are used in most fuel production pathways and, for all alternative fuels, gasoline or diesel fuel represents the reference fuel to which the alternative fuel is compared.

This GHGenius update has involved an in depth review of the emissions associated with oil refining and oil production in Canada. This included some structural changes to the model and the inclusion of more real world data and fewer assumptions. The oil refining section of the model now has time series of data for energy consumption in Canadian oil refineries, a revised and improved method for calculating the allocation of the emissions between products, and increased flexibility for modelling a specific refinery. It also has revised factors for estimating the emissions when the crude oil properties are changed.

The model results, in most cases, are aligned with the reported GHG emissions from refineries. The CAC emission factors for CO, SOx and NOx have also been adjusted to better match the reported emissions. The previous factors were based on early sector wide emissions and, now that these emissions are reported annually, clearer trends have emerged.

This new version of GHGenius provides the capacity for refiners to model individual refineries relatively easily. The cells where users could input their own data are clearly distinguished and it is no longer necessary to override any of the data or calculations in GHGenius to model a specific refinery. Most of the other LCA models don’t have this capability.

The model also allows refiners to change the refined products slate when they change the crude oil charged. Combined with the new allocation system, this will calculate any benefits that different crude oils may provide in terms of more of the high intensity products produced at the expense of the low value, low allocation, heavy products.


Tags: Crude Oil - GHGenius 3.20 - Refining
 Oil Production Update
 April 2011
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As more focus is placed on different industrial processes, more data on those systems becomes available. The crude oil systems in GHGenius have been updated several times but the most recent update was two years ago and more data is now available. It was therefore appropriate to undertake a thorough review and update of the crude oil pathways in GHGenius. The scope of this work is discussed below.

1. International Crude Oil Supply to Canada
About 50% of the crude oil that is refined in Canada is imported. The quantity of crude oil and its destination is reported by Statistics Canada and a time series of data has been produced for each of the regions in the model from 1985 to the present time. This data has been added to the model (sheet Z) and used as the basis for future projections.

2. Energy Requirements International Crude Production
The energy required to produce international crude oils has been essentially static in GHGenius (sheet S). A time series of data is produced by the International Oil and Gas Producers Association. It covers about one third of the world’s crude oil production. Data is available from 2001 to 2009. This data has been analyzed to establish some trends for the model.

3. Canadian Crude Oil Production Trends
GHGenius does not currently have total crude oil production data for Canada. The model has focussed on the crude oils that are refined in Canada. Since there is some interest in the emissions impact of crude oils that are exported from Canada this data has been added to the model.

4. Energy Requirements for Canadian Crude Oils
The energy requirements for producing conventional Canadian crude oil have been reported by CAPP and that data has been used in GHGenius. It has been reviewed and newer sources of information have been sought.

A time series of data on mineable oil sands statistics has been found that is produced by Alberta Energy Resources Conservation Board (ERCB). It includes complete mass and energy balance information for all operating mineable projects. The data has been reviewed and incorporated into the model.

Data on in-situ oil production is starting to be reported by ERCB and the data for 2009 and 2010 has been used for the model.

Another stage in the lifecycle has been added to the model to differentiate the emissions from bitumen production and bitumen upgrading. This has been done on sheet I, the Energy Balance sheet, and the Upstream Emission sheets (HHV and LHV).

5. Venting and Flaring Emissions Canadian Crude Oils
The venting and flaring data for Canadian crude oil production was updated in 2004. At that time there had been a concerted effort by the industry to reduce these emissions and some success was evident from the data. A projection of future improvements was made based on the trends. More recent data provided by Alberta Energy suggests that the annual improvements ceased about 2006, so this issue has been re-evaluated in the model.

6. Carbon Capture and Storage
Carbon Capture and Storage (CCS) is one GHG emission reduction strategy that is potentially applicable to emission sources. Bitumen upgrading and synthetic oil production are two potential applications of this technology. The carbon capture and storage technology can also be applied in the refineries and GHGenius has had the capacity to model CCS for these sectors for some time. This function in the model is reviewed, updated, and documented.

7. Co-generation of Power and Steam
Some oil sands upgraders produce their own power and export some back to the grid. This can be handled in GHGenius through the use of a negative consumption of electricity but this does not provide any flexibility in how it is modelled. In other systems that produce electricity as a co-product the model provides full flexibility, this allows the modeller to choose what power source is being displaced; this approach has been extended to upgrader operations.

8. Refinery Energy Use
The energy use data for refineries in GHGenius had been based on 2002 data and significant changes in the industry have taken place since then, such as the introduction of low sulphur gasoline and ultra low sulphur diesel fuel. This work has tried to develop consistent regional data sets, and time series of data, which can be used in the GHGenius model.

A literature review has been undertaken on changes in refining energy requirements with changes in crude density and sulphur content and changes in the way that the model deals with these issues has been made.

9. Refinery Energy Use Allocation
The other important parameter related to refineries is the allocation of emissions between products. The current allocation process has been reviewed and significant changes have been made to make the process more functional.


Tags: Crude Oil - GHGenius 3.20 - Refining
 US Update
 April 2011
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The report covers work on the regionalization of the US fuel production pathways and the updating of the basic data that covers US electric power production, US natural gas production and flows, US crude oil production and flows, and the US petroleum refining sector. The revised model resulting from this work has more functionality for modelling various scenarios in the US and more up to date data on the traditional US energy sector.

This report has been prepared to document the changes that have been made to GHGenius in terms of updating US data and the regionalization of some of that data. The version of the model that accompanies this report is GHGenius 3.20.

This work added US regional buttons to the Input sheet, these install regional values much like the Canadian regional and Provincial buttons. This makes it much easier to run US regional scenarios.


Tags: Crude Oil - Electricity - GHGenius 3.20 - Refining
 Pyrolysis Oils
 March 2011
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There is increased interest in the production and use of pyrolysis oils (or bio-oil) as a means of converting solid biomass into liquid fuels. While pyrolysis oil is not suitable for direct use as a transportation fuel, it can be used in external combustion devices such as boilers, heaters, and turbines. There are also activities underway to refine pyrolysis oil to gasoline and diesel fuel components.

The scope of this work included;
1. Pyrolysis oil production from wood or agricultural cellulosic feedstocks. Both of these feedstock families were already included in GHGenius. There are three wood options: wood waste, short rotation forestry, and harvested wood from natural forests. There are four agricultural cellulosic feedstocks in the model: wheat straw, corn stover, switchgrass, and hay. The model has been expanded so that pyrolysis oil can be made from any of these feedstocks. The emissions from the production of these bio-oils have been added to the Upstream Results sheet.
2. The produced pyrolysis oil could be used in external combustion devices such as heaters and boilers (sheet AD and sheet N). Sheet J has also been expanded so that the emissions from the production of electricity in a turbine system can be modelled.
3. A pathway that refines the pyrolysis oils to blending stock for gasoline and diesel fuel has also been added to the model.

All of the existing functionality, e.g. sensitivity solver, Monte Carlo tool, etc, in GHGenius has been maintained. The version of GHGenius that accompanies this report is GHGenius version 3.20.


Tags: Ag Residues - GHGenius 3.20 - PyrolysisOils - Wood
 2010 Biofuel Analysis
 Prepared December 2010
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In 2006, GHGenius was used to analyze the GHG emissions from the biodiesel and fuel ethanol pathways of interest in Canada. That project also investigated the sensitivity of the emissions to some of the parameters than could vary from project to project. That report has been used by a number of government departments, as they study the development of the industry in Canada. It is also one of the more popular reports on the GHGenius website.

Our knowledge of the performance of the biofuels industry in Canada has improved significantly in the four years since that work was done. In addition, GHGenius has been updated with better data on the biofuel production process and feedstock production systems. An updated report is therefore warranted, given the continued interest in the subject and the updated modelling data. The report will also serve as an updated documentation resource for these pathways in the model.

The biofuel pathways that have been analyzed in this report include five ethanol pathways, corn, wheat, barley and sugar cane based systems and a cellulosic ethanol system based on wheat straw feedstock. Six biodiesel feedstocks have been considered, canola, soybeans, tallow, used cooking oil, palm oil and jatropha. Four feedstocks have been considered for hydrotreated oils, palm, canola, tallow, and soybean oil.

For the sensitivity analysis, the focus has been on the issues that can vary from plant to plant, such as co-product drying, the use of combined heat and power, and the energy source for the thermal energy. In addition, issues that still have some uncertainty, such as changes in soil carbon are evaluated.

Tags: Barley - Biodiesel - Canola - Corn - Ethanol - HRD - Jatropha - Palm Oil - Soybeans - Sugar Cane - Tallow - Used Cooking Oil - Wheat - Wheat Straw
 Crude Oil Land Use Emissions
 Prepared September 2009
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The intense discussion concerning indirect land use change emissions for biofuels has highlighted the lack of analysis of indirect emissions associated with crude oil production and possible gaps in the assessment of direct land use change from crude oil production.

This work looked for Canadian data sources to determine if good original source data is available to make more accurate, current estimates of direct land use change associated with crude oil production. This report identifies the issues, discusses the available data and data gaps, and suggests a path forward.

Land use change emissions from the production of oil and gas in Canada are probably a relatively small fraction of the total lifecycle emissions of petroleum products in most cases. There has been some work done on quantifying the amount of land disturbed by the various activities that are undertaken prior to production commencing. This quantification does require some assumptions to be made but a variety of estimates found in the literature do cluster in a relatively narrow range.

The results of the estimates reveal that the land disturbed by oil sands extraction may be lower per unit of production than that for conventional oil and gas production. The oil sands operations have higher productivity than oil and gas wells in Alberta and thus while the concentration of the disturbed land is higher than conventional oil production, the intensity of land disturbed per unit of oil produced appears to be lower.

While there is still a large degree of uncertainty with respect to land use emissions from petroleum production, it is clear that there are some land use emissions from all sources of petroleum production in Canada. Land disturbances in other parts of the world are expected to be at least similar to those in Canada but the GHG emissions will vary widely with local conditions. Tropical forests may contain above ground biomass quantities that are an order of magnitude higher than that found in the boreal forests of Canada. This will have a significant impact on land use emissions. Soil carbon levels are not expected to have as wide a range as above ground biomass but wetlands and peat soils do have high carbon contents and petroleum production in these regions can be expected to have higher GHG emissions from land use than well drained soils.

More work should be undertaken to determine accurate values for the change in carbon inventory in the lands impacted by oil production. This is particularly important for oil sands mining where significant overburden is removed.

Tags: Crude Oil - Land Use
 Sources de Gaz Naturel Non Classique au Modèle GHG
 Prepared March 2010
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La production canadienne de gaz naturel classique diminue et devrait continuer de baisser au cours des prochaines années. Dans ses dernières prévisions, l’Office national de l’énergie s’attend à ce que le méthane de houille, le gaz de schiste et finalement le gaz des régions pionnières complètent l’offre décroissante de gaz classique et de gaz de formation imperméable comme le montre la figure suivante.

L’objectif du présent travail était de modifier les voies du gaz naturel dans le modèle GHGenius pour tenir compte des différentes sources de gaz naturel. L’utilisateur du modèle GHGenius peut maintenant choisir de modéliser chaque type de gaz naturel individuellement ou un mélange des différents types de production. Les données servant à la modélisation de ces différentes sources de gaz n’étaient pas aussi bien étoffées que celles pour les gaz classiques et le modèle a été conçu afin de faciliter le changement des entrées au fur et à mesure que de meilleures données sont disponibles.


Tags: Coal Bed Methane - GHGenius 3.18 - Natural Gas - Shale Gas
 GHGenius 3.19 Input Checklist
 August 2010
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This is a two page handout that details the user inputs on the Input sheet. Note that it is not comprehensive enough to replace any of the existing documentation, but meant as a quick checklist for model runs. It is accurate for version 3.16 of the model.


Tags: GHGenius 3.19
 Feedstock Emissions
 Prepared August 2010
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There is increased interest in the emissions associated with feedstock production for use in LCA work for non-transportation fuel pathways. For example, work has been done on establishing the carbon footprint for some livestock species as well as for milk production. All of this work needs either to include the production of feedstocks like corn, barley, and other feed grains, or to have emission factors for these materials.

This work has developed a new output sheet for the emissions from the production of feedstocks. The emissions are presented on the basis of g/tonne feedstock delivered. The emissions are presented for all of the GHG and CAC emissions in the model. The results from this work could be used as emission factors for other LCA work being undertaken by others.

This work will also allow for easier comparison to some of the emission estimates that have been produced by Agriculture Canada for feedstock production. This comparison should eventually allow for some reconciliation between the GHGenius estimates and the work that Agriculture Canada has been doing using somewhat different data sources.

The data in GHGenius is continually being updated as new information becomes available. Three new data sources have been identified for areas of the model that have not been updated for many years. These are the energy required to manufacture vehicles, the energy required to manufacture farm tractors, and some new information on canola production and crushing. All three changes result in some reduction in emissions for the fuel pathways that are impacted.


Tags: Canola - Feedstock - GHGenius 3.19
 Compte rendu du GHGenius 2008
 Prepared Août 2008
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Ces travaux ont examiné des sources de données mises à jour et, le cas échéant, ont mis à jour GHGenius. Les travaux comprenaient les suivants :
1. l’Office national de l’énergie a publié son rapport intitulé L’avenir énergétique du Canada en novembre 2007. Une version précédente de ce rapport a servi à modéliser les sources d’énergie électrique de chacune des provinces. Le dernier rapport a été examiné et les prévisions du GHGenius ont été mises à jour afin d’être en harmonie avec le scénario de référence contenu dans la documentation de l’ONE;
2. le GIEC utilise des potentiels de réchauffement planétaire différents de ceux du modèle. Un commutateur a été ajouté permettant maintenant de choisir les valeurs du modèle précédent (de 1996), les valeurs de 2001 ou les valeurs plus récentes de 2007;
3. certains autres modèles de calculs sur le cycle de vie utilisent le pouvoir calorifique inférieur (PCI) comme mesure de base. La comparaison des résultats du GHGenius (fondés sur un pouvoir calorifique supérieur (PCS)) avec ceux des autres modèles est par conséquent plus difficile. Une feuille de résultats en amont supplémentaire a été ajoutée afin de présenter les résultats en fonction des PCI;
4. dans le dernier projet sur les émissions de PCA, l’énergie intrinsèque de certains équipements a été examinée. Cet examen était incomplet, car toutes les sources d’énergie intrinsèque n’ont pu être trouvées sur la feuille N. Par conséquent, cet examen est terminé et les changements appropriés ont été apportés;
5. le GHGenius a des valeurs par défaut pour la production de pétrole synthétique selon un processus d’exploitation minière. On produit de plus en plus de pétrole synthétique grâce à l’extraction minière sur le chantier. Par conséquent, des valeurs par défaut de ce système de production ont été ajoutées. Nous avons tenu compte des prévisions de production pour les deux méthodes et nous avons élaboré des équations pour modéliser les sources prévues pour les années à venir;
6. les prévisions de l’ONE avaient également des renseignements sur le pétrole synthétique raffiné au Canada. Ces renseignements ne sont pas aussi détaillés que ceux du GHGenius, mais ceux-ci ont été examinés afin de s’assurer qu’ils correspondent aux valeurs du GHGenius;
7. le document d’orientation 2006 du GIEC apporte de légères modifications aux sources de N2O qui doivent être calculées dans le cadre d’un inventaire national. Cela comprend les émissions de N2O provenant d’une perte de teneur en carbone dans le sol. Cette source a été ajoutée au modèle en plus d’une mise à jour des valeurs par défaut du GIEC;
8. un compte rendu sur la question des émissions de N2O provenant des cultures qui fixent leur propre azote a été ajouté. Il s’agit d’une importante source d’émissions de biodiesel et l’ampleur de ces émissions évoque encore beaucoup de confusion et d’incertitude. L’approche mise à jour a été incluse dans le modèle;
9. Environnement Canada et Agriculture et Agroalimentaire Canada ont fait des progrès importants concernant la définition de facteurs d’émission appropriés pour les activités agricoles comme l’application d’engrais, les pratiques de culture et autres activités relatives à l’utilisation du sol plutôt que de se servir des valeurs de première catégorie du GIEC. Ces données ont été examinées et les meilleures données ont été choisies pour GHGenius. Ces facteurs d’émission, que l’on retrouve principalement sur la feuille W, ont maintenant été régionalisés. Cela signifie quelques changements structurels du modèle;
10. les changements dans les calculs de la teneur en carbone dans le sol du modèle ont été modifiés pour une approche plus directe. Les calculs du modèle étaient fondés sur l’approche Delucchi, qui tient non seulement compte du principal changement dans la teneur en carbone dans le sol fondé un changement de la gestion initiale, mais également d’un changement secondaire escompté fondé sur la réversion éventuelle de la modification principale de l’utilisation du sol. Ce changement secondaire a été supprimé des calculs;
11. dans le modèle, nous avons une contrepartie de la fixation du carbone aérien en raison de la fertilisation azotée de la biomasse provenant d’engrais perdu hors site. Ceci n’est pas inclus dans les lignes directrices du GIEC. Nous avons modifié le modèle afin que l’utilisateur puisse inclure ou exclure cette source des calculs;
12. une discussion relative aux changements dans la teneur en carbone aérien et souterrain a été incluse. Le modèle a été modifié afin que les crédits liés à l’hypothèse de l’utilisation du sol relative aux coproduits d’éthanol correspondent aux calculs de l’énergie et des crédits d’émissions de GES. Une discussion relative à la façon de modéliser les modifications directes et indirectes de l’utilisation du sol pour les matières premières de la biomasse est incluse.

Préparé pour le compte de : Ressources naturelles Canad
 Sensibilité Des Émissions De GES Liées Aux Biocar
 Préparé Août 2006
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Ce projet vise à déterminer les principaux facteurs qui influent sur les émissions de gaz à effet de serre (GES) durant le cycle de vie des filières actuelles de production de l’éthanol et du biodiesel. Les responsables de politiques et les producteurs, les distributeurs, les détaillants et les consommateurs de carburant peuvent utiliser cette information pour prendre des décisions ayant des répercussions positives sur la performance des carburants renouvelables en matière d’émissions de GES durant le cycle de vie.

Il existe trois principaux moyens de réduire les émissions de GES dans le secteur des transports : l’amélioration de l’efficacité énergétique à tous les stades du cycle de vie, l’utilisation de carburants à intensité plus faible d’émissions de carbone ou le changement des modes de transport. Il est également possible de combiner les trois solutions.

Les producteurs de carburants renouvelables ont un certain degré de maîtrise sur les deux premières catégories. Toutefois, durant la conception et la construction des installations de biocarburant, ils cherchent à maximiser le rendement des investissements sans forcément chercher à réduire les émissions de GES. Ces installations sont donc souvent éconergétiques, mais les types d’énergie qui y sont utilisés ne sont pas nécessairement optimaux.

Le scénario de référence qui est modélisé représente essentiellement la situation en fonction des valeurs par défaut de la version 3.4 de GHGenius. Ces valeurs ont été choisies par le passé de façon à représenter soit les activités types des exploitations existantes au Canada, soit les activités probables des exploitations qui pourraient exister au Canada. Le choix des valeurs par défaut des émissions attribuables à l’utilisation des terres fait cependant exception à cette règle.
Pour les besoins de ce projet, nous nous intéressons entre autres aux répercussions des émissions qui pourraient découler des différentes pratiques agricoles. Ces pratiques pourraient entraîner des fluctuations du carbone du sol ainsi que des changements de la biomasse aérienne. Les valeurs par défaut ont donc été établies de telle façon que le scénario de référence ne présente aucune fluctuation du carbone du sol, aucun changement de la biomasse aérienne découlant d’un meilleur rendement de culture et aucune croissance de la biomasse due à la fertilisation par l’écoulement d’azote provenant de l’extérieur du site.

Préparé pour le compte de : Ressources naturelles Canad
 Biomass to gasoline and DME
 Prepared March 2010
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This work introduces two new pathways to GHGenius, wood to gasoline, and wood to dimethyl ether (DME) and expands the options for producing the wood feedstock to include the harvesting of timber from managed forests.

The interest in biofuels continues to grow and technology developers are considering the opportunities of producing fuels, other than ethanol and biodiesel, which can be used in either blends with petroleum fuels, or as replacements for petroleum fuels. It is important that an understanding of the environmental implications of these technologies be understood before the fuels are introduced to the marketplace. This work helps to address that need by expanding GHGenius to consider two new biomass to fuel processes.

Bio-gasoline can be produced in a multistep process to gasify wood, convert the syngas to methanol or DME and then convert the methanol or DME to gasoline. The back end of the process is similar to the natural gas to methanol to gasoline process that was operated commercially in New Zealand in the 1990s by Mobil.

Bio-DME can also be produced through a similar route except without the last step of converting the DME to gasoline. DME has interesting fuel properties, including a high cetane number, and no carbon carbon bonds. It thus has potential as a replacement diesel fuel as well as a potential hydrogen carrier.

Wood feedstock in GHGenius has been represented by short rotation forestry (SRF) or by wood residues. A switch on the input sheet has been used to choose which feedstock is active in the model. There has been increased interest in harvesting timber for energy applications rather than for fibre so this third option has been added to the model. This option is now active for all of the wood to energy pathways in the model, not just the two that have been added here.
The new version of the model that accompanies this report is version 3.18.


Tags: DME - GHGenius 3.18 - Gasoline - Wood
 Unconventional Natural Gas
 Prepared March 2010
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Canadian production of conventional natural gas is declining and is expected to continue to decline over the next few years. The National Energy Board, in their latest forecast, sees coal bed methane, shale gas, and eventually frontier gas supplementing declining supplies of conventional gas and tight gas.

The goal of this work was to modify the natural gas pathways in GHGenius to accommodate these different sources of natural gas. The GHGenius user can now choose to model each type of natural gas individually or a blend of the different types of production. The data used for modelling these different sources of gas was not as well developed as the data for conventional gas and the model has been developed so that it is easy to change these inputs as better data become available.


Tags: Coal Bed Methane - GHGenius 3.18 - Natural Gas - Shale Gas
 Algae GHG Emissions
 Prepared March 2010
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As a result of a number of recent technical, social, economic and environmental developments, such as the food vs. fuel debate, land use change, rising fossil energy prices, and other issues, there is an increasing interest in finding new biomass feedstock sources that can meet the need for increasing use of biomass for energy without having the negative impacts being reported. One feedstock that has received considerable interest as a biomass source, in particular for transport applications, has been algae. Algae presents a number of potential benefits including the potential to produce 10 times more oil per acre than terrestrial plants, can grow in areas that agriculture crops can’t, such as brackish water and marginal land, they thrive on waste CO2 and nutrients from agricultural run off and municipal wastewater, they are not used as food or feed, and in addition to use as a fuel, a number of valuable co-products can also be produced.

Despite the significant interest in algae production systems, there is relatively little known about the actual lifecycle emissions associated with algae production. A very flexible algae production and algae to biodiesel pathway was added to GHGenius in 2009. The model can handle a wide range of inputs into the algae production system, separate inputs for the algae oil extraction and for the biodiesel production processes. The non-oil portion of the algae can be used for fertilizer, animal feed, or energy production. The model is thus well positioned to analyze a wide variety of algae systems.
The current default data in the model is based on information from three studies that quantified the inputs and outputs for micro algae production in open ponds and undertook an estimation of the GHG emissions. One study was conducted by NREL (2001), another was an Australian study undertaken by Campbell et al (2009), and the third was a French study (Lardon, et al., 2009). Each study considers a very different system so comparisons between the studies are difficult to make. There is a wide range in the projected GHG emissions in the three studies.

With the continued interest in algae production, there is a steady release of new data on the techno-economical parameters of algae production becoming available. Two aspects of this project have been the review of recent literature and discussions with algae researchers. In addition, researchers are also now moving to the stage where some feedback on the overall performance of their systems would be valuable.

A number of new papers have been identified that have considered the energy and environmental impacts of various algae production systems. There continues to be wide variation in the projected performance of algae and algal oil systems. This is not unexpected given the theoretical nature of the analysis that has been undertaken. None of the papers was based on the actual measured performance of any commercial system.

Some of the difference in the results is a function of different assumptions concerning system boundaries. While the biomass production rates of algal systems can be significant, a major issue for the systems will be the relatively low concentration of biomass in the production systems. This means that very large quantities of water must be used in the systems, the energy requirements for moving, processing, and treating the water are also large and will be critical to the overall environmental performance of the systems.


Tags: Algae
 Freight Emissions Report
 Prepared February 2010
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This work introduces and develops a new functional unit to GHGenius so that the tool can be applied to freight transportation systems that are employed to move goods. This function unit is a tonne-km, being the movement of one tonne of goods one kilometre. Previously only a unit of energy or the distance travelled were used as functional units in GHGenius. This new measure provides a reference for very different systems that are commonly used to move freight.

This work includes a new output sheet for the results. The emissions are presented for all of the GHG and CAC emissions in the model.

The modes of freight that have been included are truck, rail, marine and some preliminary information for airfreight. Multiple classes for marine transportation are presented, as there is a difference in emissions according to vessel type and size.

The fuels that have been included are marine/rail fuel oil, ultra low sulphur diesel fuel, gasoline, biodiesel, hydrotreated renewable diesel, and CNG and LNG. Not all fuels are used for all modes of transportation. It has been necessary to split the existing combined CNG/LNG column on the Upstream Results sheet into two columns so that LNG data is always available. The capacity to model these additional fuels provides considerable additional information for the user.


Tags: Freight - GHGenius 3.17
 Victoria GHGenius Workshop Presentation
 November 2009
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Here is the presentation used for the Victoria GHGenius Workshop.


Tags: Workshop
 GHGenius 3.16 Input Checklist
 Prepared October 2009
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This is a two page handout that details the user inputs on the Input sheet. Note that it is not comprehensive enough to replace any of the existing documentation, but meant as a quick checklist for model runs. It is accurate for version 3.16 of the model.


Tags: GHGenius 3.16 - Manual
 2009 GHGenius Update
 Prepared September 2009
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This report covers an update of the following data in GHGenius. In some cases the structure of the model has been slightly changed to accommodate the time series but any difference in the results is due to the new input data and not due to any structural changes in the way that the model handles the new data.

1. International crude oil energy and emissions. Several new data sources have recently been identified. These include the International Oil and Gas Association (a time series from 2002 of the energy and GHG emissions of crude oil production for various regions of the world), data from the Alberta Energy Research Institute studies (some specific useful information for countries such as Mexico, Venezuela, Iraq, and Saudi Arabia), and the World Bank flaring study.

2. Canadian electricity. A time series of electric power production on a regional basis in Canada from 2000 has been be developed from Statistics Canada data. Regional generation efficiencies and proportions of power types have been extracted from the data.

3. Rail energy. Statistics Canada has a time series data for freight movement on Class 1 railways. This data has been compared to similar information from the United States and incorporated in the model.

4. Potash mining. Statistics Canada, CIEEDAC, and NRCan Comprehensive Energy Use Database provide a time series for energy consumption, quantity and type. This has been compared to the NRCan CIPEC report that was used as a data source in the model. The new information has been incorporated into the model.

5. Nitrogen fertilizer. Statistics Canada, CIEEDAC, and NRCan Comprehensive Energy Use Database all have a time series for information on this sector. These data sets do not include process energy consumption but that can be calculated. The data sets have been compared to the NRCan CIPEC report that was the base of data in the model.

6. Corn and Soybeans. Fertilizer and yield time series available from the USDA. Some Statistics Canada yield data on these crops and other Canadian crops is available as well. This time series data has been incorporated into the model.

7. Ethanol and Biodiesel energy requirements. New data from the United States is available for both these alternative fuels. An update and development of a time series for ethanol has been incorporated into the model.

8. Some users have identified a number of enhancements for the functioning of the EV macro in GHGenius. These modifications have been incorporated into GHGenius. They provide more functionality and having them in the public model will allow them to be continually updated as model enhancements are undertaken.

9. Natural gas update. The Canadian Gas Association has provided some recent information on distribution emissions. Unfortunately the report did not provide activity data but that that has been developed from other sources. In addition Statistics Canada has data on the natural gas sector and this will be reviewed to see if it can be worked into the model.


Tags: Biodiesel - Corn - Crude Oil - Electricity - Ethanol - Fertilizer - GHGenius 3.16 - Natural Gas - Soybeans
 Algae and Jatropha Biodiesel
 Prepared September 2009
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The cultivation and crushing of Jatropha has been added to the GHGenius model. Similar to other vegetable oil pathways in GHGenius, the Jatropha seeds are crushed to produce the oil and the oil is then transesterified to produce biodiesel.

There is increased interest in the production and use of algae to produce fuels for the transportation sector. In spite of all of the interest, there has been very little quantification of the energy and emission benefits of such an algae to biodiesel pathway published, although there is some information on the energy and material balances of some of the proposed algae systems. The available literature on algae production systems has been reviewed to gather the data that is required for modelling and the data has been added to an algae to oil pathway and an algae oil to biodiesel pathway, the same combination of systems that we use for other biodiesel systems, in the GHGenius model.

The GHGenius has been modified so that the SuperCetane pathway that was in the model and could process both tallow and canola oil can now process all eight types of vegetable oils or animal fats. The data that is used for the process is now the operating information for the Neste NExBTL and the SuperCetane description has been replaced by a more generic description of hydrotreated renewable diesel (HRD).

The version of the GHGenius model that accompanies this work is version 3.16. There are other changes that have been made to version 3.16 to update the data in the model but these are described in a separate report.


Tags: Algae - Biodiesel - GHGenius 3.16 - HRD - Jatropha - SuperCetane
 Biomethane Report
 Prepared March 2009
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There is increased interest in bio-methane in the transportation sector. This term means different things to different people but would typically include landfill gas and the output from anaerobic digestion. The production of landfill gas is already included in GHGenius as a feedstock for methanol production but for this work its use is expanded to compress the gas and use it as CNG or liquefy it to produce LNG.

Anaerobic digestion of biological waste and agricultural residues is a rapidly expanding industry in Europe. Part of the drive there is a result of significant government incentives but there will be situations in Canada where environmental requirements drive the application of this technology and we will likely see some further expansion of the availability of bio-methane in Canada.

Anaerobic digestion (AD) is applied using mostly waste materials (manure, food and beverage wastes, etc.) rather then AD involving feedstocks grown specifically to produce biogas. It is unlikely that this later option will be adopted in North America unless the value of the biogas approaches that in the very heavily subsidized European markets. Hay, as a substitute for silage, as a feedstock for the AD process is modelled so that some sensitivity to non-waste products can be accessed. The structure of the model has been set so that any of the four agricultural residues can be used as a feedstock, either alone or in combination with manure.

The gas from the landfill or the digester will be cleaned up and either compressed or liquefied so that it can be used as a transportation fuel for light and heavy-duty applications.
The necessary changes have been made to the macros to ensure that the sensitivity solvers, Monte Carlo simulator, and other macros all function with the additional sheet in the model. The version of the model that accompanies this report is 3.15.


Tags: Anaerobic Digestion - Biomethane - GHGenius 3.15 - LFG
 Provincial Default Values
 Prepared March 2009
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The recent strategic development plan for GHGenius identified a need for “Provincial” versions of GHGenius. Some of the provinces wish to develop their own versions of GHGenius so that they can establish the default values for various fuels.

This has been accomplished by the addition of a series of default buttons on the Input Sheet. These buttons change the model to the appropriate region, the correct provincial power mix, the appropriate crude oil slate, and the correct petroleum product distribution patterns. Like other default values in the model the user can still overwrite the cells so that no functionality is lost. There are default values developed for BC, Alberta, Saskatchewan, Manitoba, Ontario, Quebec, and Atlantic Canada.

The defaults have been set up for the various commercial fuels, gasoline, diesel, natural gas, LPG, ethanol, and biodiesel. In some cases, reasonable estimates will be made of what is likely to happen in the province with respect to the supply of ethanol and biodiesel. The version of the model that accompanies this report is 3.15.


Tags: Biodiesel - Diesel - Ethanol - GHGenius 3.15 - Gasoline - Provincial Defaults
 March 2009 Workshop Handout
 Prepared March 2009
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The GHGenius summary that was used at the March 2009 workshops.


Tags: Workshop
 Barley and Peas Ethanol Report
 Prepared November 2008
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Barley and peas both have some attractive features as ethanol feedstocks in western Canada and there are some groups considering using these feedstocks for commercial ethanol production. Barley is used in some European ethanol plants but to the best of our knowledge peas are not used commercially for ethanol anywhere in the world.

The goal of this work was to add barley and production to GHGenius and to use both of these feedstocks individually to produce ethanol.

Agronomic data on barley and peas production in western Canada has been identified from public sources and added to the model.

The new feedstocks have been added to all of the existing uses of ethanol in the model other than the fuel cell pathways. This is consistent with other ethanol pathways that have been added to the model recently.


Tags: Barley - Ethanol - GHGenius 3.14 - Peas
 2008 GHGenius Update
 Prepared August 2008
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There have been a significant number of changes made to the model. The new version is 3.13. These upgrades have included incorporating more recent forecasts of future changes in the Canadian energy infrastructure, the capability of having a choice of GWPs to make comparisons with other studies and models easier, the ability to report emissions per unit of energy on either a higher or lower heating value basis, much expanded capacity to model oil sands operations, many changes to the land use calculations to make the results more regional specific, and a number of smaller changes. The changes have impacted all of the pathways in the model.

The two largest pieces of work included:
1. GHGenius has had default values for the production of synthetic crude oil by an integrated mining process. More and more synthetic crude oil is being produced by in situ mining (Steam Assisted Gravity Drainage or Cyclic Steam Stimulation), so pathways and default values for these alternate production systems have been added. There is now full flexibility in the model for combining bitumen extraction methods and integrated or stand alone upgraders.
2. A major upgrade of the methodology for calculating land use emissions (direct and indirect).
a. The IPCC 2006 guidance document has some small changes in the sources of N2O that are to be calculated as part of a national inventory. This includes N2O emissions resulting from a loss of soil carbon. This source has been added to the model along with an update of the IPCC default values.
b. An update on the issue of N2O emissions from crops that fix their own nitrogen has been included. There has been an update of the approach included in the model.
c. Environment Canada and Agriculture and Agri-Food Canada have made considerable progress in defining the appropriate regional emission factors for agricultural activities such as fertilizer application, cultivation practices and other land use activities rather than relying on the IPCC Tier 1 values. These emission factors, which are found mostly on sheet W, have now been regionalized.
d. The soil carbon changes calculations in the model have been changed to a more straightforward approach.
e. Within the model we have an above ground carbon offset due to nitrogen fertilization of biomass from fertilizer that is lost offsite. This is not included in the IPCC guidelines. We have modified the model so that this source can be included or excluded from the calculations by the user.
f. A discussion of above and below ground carbon changes has been included. The model has been modified so that the land use assumption for ethanol co-product credits are consistent with the energy and GHG emission credit calculations. A discussion of how to model both the direct and indirect land use changes for the biomass feedstocks in included.


Tags: Biodiesel - Canola - Corn - Crude Oil - Electricity - Ethanol - GHGenius 3.13 - Land Use - Soybeans - Wheat
 Bioethanol LCA Models
 Prepared March 2008
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Environmental life cycle analysis (LCA) models are complex tools that can be applied to help assess the relative attractiveness of transportation fuels and other products. LCA can be used to inform government policy makers, industry, community stakeholders and other groups in making more informed decisions for the Canadian and regional environment. However, there are a variety of models available and results from these vary, mostly for valid reasons. This can reduce confidence in LCA results for ethanol. In addition, the quality of conclusions drawn from LCA models are influenced by the assumptions and data used in generating model results. As a result, Environment Canada needed to develop a better understanding of LCA models available as well as the underlying data, assumptions and associated calculations these tools use to generate results.

The main overall purpose of this report is to provide an assessment of existing LCA models that can be applied in determining the environmental footprint of bioethanol and competing fuels (e.g., gasoline) in Canada. The assessment includes analysis to identify the key factors that contribute to differences in the results from different models. This report also provides: an analysis regarding the role of LCA in policy formulation; an overview of other modelling activities oriented to environmental policy development; an overview of what LCA is and how it works; a brief description, assessment and availability (for the purposes and scope of this study) of 37 LCA models, more detailed analysis of 9 models and selection of 2 models for detailed analysis and comparison; sensitivity analysis using one model to identify the factors in the life cycle that most strongly influence LCA results; and recommendations regarding the development and enhancement of LCA modelling for Canada.

Tags: Corn - Ethanol - Wheat
 Biodiesel LCA Models
 Prepared March 2008
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Environmental life cycle analysis (LCA) models are complex tools that can be applied to help assess the relative attractiveness of transportation fuels and other products. LCA can be used to inform government policy makers, industry, community stakeholders and other groups in making the best decisions possible for the Canadian and regional environment. However, there are a variety of models available and results from these vary, mostly for valid reasons. This can reduce confidence in LCA results for biodiesel. In addition, the quality of conclusions drawn from LCA models are influenced by the assumptions and data used in generating model results. As a result, Environment Canada needed to develop a better understanding of LCA models available as well as the underlying data, assumptions and associated calculations these tools use to generate results.

The main overall purpose of this report is to provide an assessment of existing LCA models that can be applied in determining the environmental footprint of biodiesel and competing fuels (e.g., diesel) in Canada. The assessment includes analysis to identify the key factors that contribute to differences in the results from different models. This report also provides: an analysis regarding the role of LCA in policy formulation; an overview of other modelling activities oriented to environmental policy development; an overview of what LCA is and how it works; a brief description, assessment and availability (for the purposes and scope of this study) of 37 LCA models, more detailed analysis of 9 models and selection of 2 models for detailed analysis and comparison; sensitivity analysis using one model to identify the factors in the life cycle that most strongly influence LCA results; and recommendations regarding the development and enhancement of LCA modelling for Canada.

Tags: Biodiesel - Canola - Soybeans
 Update of CAC Emissions in GHGenius
 Prepared March 2008
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This work primarily considered the emissions of the criteria air contaminants (CAC) calculated by the model. The emission factors used to calculate these emissions have been reviewed and updated where possible. The US AP-42 documents were the source of many of the emission factors. In some cases this involved revised estimates for methane and nitrous oxides from some combustion sources and thus the GHG emissions calculated by this version of the model have changed as well.

For the well established processes in the model, such as electric power production and oil refining, it has been possible to further regionalize the CAC emissions with different values for each region of Canada and for the United States.

There is now more consistency in how the CAC emissions are treated in the model. All of the processes where CAC’s are calculated now have a base value and the ability to change that value over time as new control strategies are implemented.

A significant amount of work was undertaken with the US EPA model NONROAD2005 to estimate the emissions from off road and stationary internal combustion engines. The N2O emission rate for all of these sources was also updated with the latest IPCC emission estimates.
There remain a number of fuel pathway technologies in GHGenius that are still under development or have not been reviewed and included in AP-42. For these systems other, probably less reliable, estimates have been identified in the literature and incorporated into the model. The estimates that have been made in these cases are considered to be conservative. Some effort has been made to have some consistency in the choice of emissions factors between similar processes.

A large number of small changes have been made to the emission factors in the model. These changes have also included further regionalization of some pathways and further differentiation of the Canadian emissions compared to the US emissions. While the focus of the work has been on the CAC emissions in a number of cases changes to methane and nitrous oxide emission factors have also been made. In addition there were some structural changes to how future emissions reductions are included in the model and the structure of how the biodiesel process emissions are calculated.

Tags: CAC Emissions - GHGenius 3.12
 GHGenius 3.14 Input Checklist
 Prepared November 2008
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This is a two page handout that details the user inputs on the Input sheet. Note that it is not comprehensive enough to replace any of the existing documentation, but meant as a quick checklist for model runs. It is accurate for version 3.14 of the model.

- Tags: GHGenius 3.14 - Manual
 Energy Balance
 Prepared November 2007
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This work entailed the addition of a new Energy Balance sheet to the model. This sheet has tables of the secondary energy by fuel type for each stage of all of the pathways. There are tables for electricity, coal, natural gas, diesel fuel, biomass, crude oil, gasoline and another table to account for minor fuels such as LPG, coke, still gas, etc. From these tables the primary energy inputs into each pathway can be calculated from existing data in the model.
This approach of producing separate tables for each type of energy and then rolling all of the data into the primary energy table will not only add some structure to the primary energy calculations but it will also yield information on the types of fuel consumed in the pathways that will have value by themselves.

Tags: Crude Oil - Electricity - Energy Balance - GHGenius 3.11 - Gasoline - Natural Gas
 US Data Update
 Prepared October 2007
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The report covers work on the regionalization of the US fuel production pathways, and the updating of the basic data that covers US electric power production, US natural gas production and flows, US crude oil production and flows, and the US petroleum refining sector. The revised model resulting from this work has more functionality for modelling various scenarios in the US and more up to date data on the traditional US energy sector.

This report has been prepared to document the changes that have been made to GHGenius in terms of updating US data and the regionalization of some of the data. The version of the model that accompanies this report is GHGenius 3.10.
There are some small changes in the average results for the United States for the fossil energy pathways as a result of this update. The results for Canada also show some very small changes as a result of updating some of the foreign oil production data and as a result of a few small structural improvements in the way that the US natural gas values are calculated.

Tags: Crude Oil - Electricity - GHGenius 3.10 - Natural Gas
 Land Use Report
 Prepared September 2007
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The purpose of this work was to identify key factors that influence the life-cycle greenhouse gas (GHG) emissions relating to land use choices of biofuel feedstocks. Emissions from land use changes are becoming a larger issue as the quantities of biofuels produced around the world grows. Concern is being raised about the soil carbon changes that might arise from bringing new land into production. Many GHG emissions models (such as GREET) ignore this aspect of the biofuels issue but some, including Dr. Mark Delucchi’s LEM and GHGenius, have the capability of including these emissions in the biofuel production pathways. The problem is that the scenarios that need to be developed are futuristic and therefore up to the modeller to select the appropriate data for modelling. This subjective approach is problematic.

This work investigates some of the issues that impact these emissions and to arrive at some potential recommendations of how the issue could be best modelled in the future.

The work only considered four feedstocks; corn, wheat, canola, and soybeans. These are the primary feedstocks for the first generation biofuels and the ones that are currently facing the greatest growth pressures. The question is where will the feedstocks to produce these biofuels come from? In GHGenius, the default values for most feedstocks assume some combination of increase in yield and substitution for some generic agricultural feedstock and while a case can be made that this has been the historical route it may not apply in the future. This work sought to address a number of questions that impact on this issue.

Avoided transportation emissions resulting from the use of biofuel feedstocks locally rather than exporting these feedstocks have generally been ignored in most discussions regarding land use and bringing more land into production in remote regions. These emissions will vary from country to country depending on transportation modes employed and the destination of customers. The preliminary analysis undertaken here indicates that in the case of Canada these transportation emissions are very large and avoiding these emissions can offset soil carbon losses resulting from brining new land into production elsewhere in the world.

The most significant issue that arose from this work was the impact of the conversion of forests and forestland to biofuel feedstock production. It is this factor that has the potential to eliminate the GHG emissions benefits of most biofuels as they are currently produced. A thorough investigation of this issue is beyond the scope of this work but an overview of the basic facts is presented.

Tags: Biodiesel - Canola - Corn - Ethanol - Land Use - Soybeans - Wheat
 Sugar Beet Ethanol Report
 Prepared June 2007
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There have been proposals recently for the construction of fuel ethanol plants that would process sugar beets as the feedstock. These proposals have been for plants in Prince Edward Island, Ontario and Quebec. Sugar beets are grown for sugar production in Alberta. In the past there were also sugar beets produced in Manitoba but this plant has been closed as they were unable to compete with very low cost imported sugar, and Canada is one of the few developed countries that does not provide protection for their domestic sugar industry. World sugar prices and grain prices have increased significantly in recent years and sugar beets may be a viable feedstock for fuel ethanol in Canada.

A few lifecycle analyses have been done on sugar beet ethanol in a European context but nothing has been done in North America. This work adds the sugar beet to ethanol pathway to GHGenius. GHGenius is now one of the few models that can compare ethanol produced from corn, wheat, sugar cane, and sugar beets based on a consistent data set. Where possible North American data on sugar beet production has been incorporated into the model.

Tags: Energy Balance - Ethanol - GHGenius 3.9 - Sugar Beet
 2007 Crude Oil GHGenius Update Report
 Prepared April 2007
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The energy requirements for the production of Canadian crude oil have been updated. The energy data for the various classes of crude oil has been extracted from the NRCan report, “Canada’s Energy Outlook: The Reference Case 2006”. and incorporated into the data on sheet S in the model.

Other sources of information were sought to validate the Outlook data. In some cases these other sources were used where the data appeared to be more complete or could be corroborated. This has provided a better profile of the energy requirements for oil production in Canada.

The type of information included for all of the different types of crude oil has been expanded to include the density and the sulphur content. This way it is now possible to determine the actual oil density and sulphur content of the oil that is going into the refinery rather than using the essentially static values that were previously in the model.

Tags: Crude Oil - GHGenius 3.8 - Natural Gas - Refining
 Bio-Butanol and a Review of Urea SCR
 Prepared February 2007
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Butanol or “Bio-Butanol” as it is sometimes referred to has recently been proposed as a gasoline additive used either with ethanol or instead of ethanol in low level gasoline blends. Butanol has a lower vapour pressure and a lower ability to absorb water when blend with gasoline and some proponents suggest that these two properties would reduce the implementation costs of low level alcohol blends by allowing the butanol to be blended with gasoline at refineries and pipelined to distribution terminals.

One butanol pathway has been added to GHGenius. A corn to butanol pathway for gasoline blends is the most appropriate pathway to consider for North America applications. This corn to butanol pathway is fully functional including summary information and cost effectiveness calculations. Additional co-products have been added to the model including acetone.

Reduction of diesel NOx emissions is difficult due to the presence of oxygen in the exhaust. In the oxidizing environment of lean exhaust, fuel has proven to be only a marginally effective reducing agent. Urea SCR systems utilize aqueous urea as a means of introducing ammonia as the NOx reduction catalyst. These systems have been shown to be less sulphur sensitive than NOx adsorbers are. The latest information on this issue has been reviewed and the new findings have been incorporated into GHGenius.

Tags: Butanol - Corn - Exhaust Emissions - GHGenius 3.7 - Urea
 Wood Ethanol Report
 Prepared November 2006
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- For wood to ethanol the process data is sparse but there is one complete set of mass and energy balance data that was developed by the US DOE several years ago that will be used as the base. This input data was also discussed with researchers at UBC who have been active in this field for many years although they have focussed on individual steps in the process rather than on the overall process. We have also reviewed the process emissions on sheet N in the model for all of the biological ethanol pathways and separated the inputs for wood, ag residues, corn and wheat ethanol.

- For wood to natural gas there is some work underway in Europe investigating and demonstrating at the pilot level the gasification of wood and the upgrading of the gas to produce pipeline quality natural gas. Some preliminary data is available. This data has been reviewed and the model has been updated to reflect recent advances.

- There is some interest in producing pipeline quality gas from coal in Canada. There is one commercial pant in the United States (Dakota Gasification) and the literature surrounding that plant has been reviewed, along with new process developments in the field, to develop the basis for a new pathway in the model. This pathway is similar to the wood to natural gas pathway and the use of the gas will include light and heavy-duty vehicles.

Tags: Coal - Ethanol - GHGenius 3.6 - Lignocellulosic - Natural Gas - Wood
 GHGenius Monte Carlo Documentation
 Prepared September 2006
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This report covers work on adding a Monte Carlo Simulation tool to the GHGenius model.

One type of spreadsheet simulation is Monte Carlo simulation, which randomly generates values for uncertain variables over and over to simulate a system. Monte Carlo simulations are a means of solving a numerical problem that cannot easily be solved by means such as integral calculus or other numerical methods. A Monte Carlo simulation involves using many iterations of random inputs to determine a set of outputs. Due to the nature of the number of calculations and repetitive algorithms Monte Carlo is a method well suited to computer simulations.

The macro allows up to five input variables to be adjusted in a single run. The user is able to set the type of distribution for each input as well as the expected mean and the standard deviation, or min and max for a uniform distribution.

The output includes the statistical data on mean, median, standard deviation, standard error, variance, skewness, and kurtosis as well as a graphical representation of the results so that the user can see visually the shape of the out distribution. One new sheet has been added to the model as part of this work.

Tags: GHGenius 3.5 - Manual - Monte Carlo
 BC Biodiesel Feedstock Study
 Prepared March 2006
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The Federal Government has included a production goal of 500 million litres of biodiesel by 2010 in its Climate Change Action plan. They have also established an $11.9 million fund that will support research and provide incentives for industrial-scale biodiesel pilot plants, and support demonstrations of its effectiveness to encourage broader use of biodiesel.

One key aspect of meeting the 500 million litre target is the identification of sufficient feedstock to convert into biodiesel. Feedstock availability is quite diverse across Canada with different regions not only producing different feedstocks but also having varying supply and demand balances. The objective of this work is to investigate these feedstock issues for the Province of British Columbia.

The specific goals of this work are therefore:
- First, to identify total volumes and types of potential British Columbia feedstock available annually to produce biodiesel (methyl ester), including identifying potential of feedstock imports and exports.
- Secondly, to identify whether British Columbia has sufficient (volume, type, availability, price) domestic biodiesel feedstock to supply a viable domestic biodiesel industry in the short and long-term, and to identify how feedstock imports and exports impact the industry.
- Finally, to evaluate other issues that might arise with some of the specific feedstocks.

Six classes of biodiesel feedstocks have been considered in this report. In five of the six cases the product is currently being sold for some application. Only in the case of trucked liquid wastes (brown grease) is the feedstock being disposed of. These non-marketed volumes are very limited. In many cases there are also imports and exports of the feedstocks.

Tags: Biodiesel - Canola - Marine Oil - Tall Oil - Tallow - Yellow Grease
 Biofuel Sensitivity Analysis
 Prepared August 2006
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The purpose of this work was to identify key factors that influence the life-cycle greenhouse gas (GHG) emissions of current ethanol and biodiesel production pathways. This information can then be used by policy makers, fuel producers, distributors, retailers and consumers to assist them in making decisions that positively impact the lifecycle GHG performance of the renewable fuels sector.

For the transportation sector there are generally three ways that GHG emissions can be reduced; improve energy efficiency at all stages of the life cycle, use lower carbon intensity fuel sources, or change transportation modes. Combinations of the three approaches are of course also possible.

Renewable fuel producers have some control over the first two categories but they will be looking to maximize the return on investment when they design and build biofuel facilities and not necessarily minimizing GHG emissions. This may lead to the facilities being energy efficient but the types of energy that are used in the facilities may not be optimized.

For this work we are interested in, among other possibilities, the emissions impact that could arise from different farming practices. These practices could result in soil carbon changes and perhaps in changes in above ground biomass. The default values for modelling have therefore been set so that in the base case there is no change in soil carbon, no change in above ground biomass arising from increased crop yields, and no biomass growth resulting from nitrogen run-off lost offsite.

Tags: Biodiesel - Canola - Corn - Ethanol - Lignocellulosic - Palm - Soybeans - Sugar Cane - SuperCetane - Tallow - Wheat - Yellow Grease
 FTD From Coal and Palm Oil Biodiesel Report
 Prepared May 2006
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Due to high oil prices and the availability of stranded gas there is increased worldwide interest in FT distillate fuels. In regions of the world, such as North America, where gas prices are higher but there are substantial reserves of coal, high oil prices and interest in FT distillate is causing an interest in coal to FT distillate processes such as is practiced in South Africa. The coal to FT distillate pathway has been added to the model. It has been added to all of the results sheets. As part of this work the FT fuels for the light duty diesel applications have been added to GHGenius as well.

Palm oil is the lowest cost vegetable oil feedstock produced in the world today. It is increasingly being considered as a feedstock for biodiesel production, not only in the regions of the world where it is produced but also in Europe and North America. The environmental benefits of palm oil are also somewhat controversial with claims regarding cultivation practices being both pro and con palm oil as a sustainable feedstock source.

The production of palm oil and palm oil biodiesel has been added to the model. The biodiesel can be used as a neat fuel and in blends in heavy-duty vehicles and in blends in light duty vehicles. All of the pathways have been added to all of the results sheets in the model including the summary sheets and the cost sheets. Palm oil biodiesel can now be compared to biodiesel produced from other oil sources within the same model.

Tags: Biodiesel - Coal - Fischer Tropsch - GHGenius 3.4 - Palm
 GHGenius Sequestration Report
 Prepared April 2006
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The report covers work on the expansion of pathways and expanding the results from existing pathways. This work involved the following tasks and deliverables.

1. The potential to include carbon dioxide sequestration to a number of feedstock and fuel production pathways has been added to the model. There was previously a switch in GHGenius to account for carbon sequestration in thermal power generation but this was a very simply approach to the issue and it underestimated the emissions in the upstream portion of power generation. There are a number of other places where sequestration might be employed. These include gasification plants, oil sands upgraders, oil refineries, methanol, and ethanol plants. The capability of adding a sequestration step to all of these facilities has been added to GHGenius and the current switch for electric power plants has been removed to calculate the impact of carbon storage more robustly for power plants.

2. The capability of using biodiesel in the light duty diesel and light duty hybrid diesel vehicles has been added to the model. These pathways have also been added to the LDV Summary sheet and the Light Duty Cost effectiveness output sheets. This involved only the combination of existing fuel and vehicle pathways in the model.

3. The tables 51c and 51e on sheet I have been expanded to include all of the pathways in the model. This included the pathways that are primarily electric in nature. It should be noted that in GHGenius, electric power is treated as a primary energy source where a kWh of power is converted to 3,600 kJ of energy. Some other models consider electric power a secondary source of energy and account for the energy of one kWh based on the energy that went in to the power plant so there may be some differences in the results shown in GHGenius compared to some other models. We may want to consider changing this in the future.

4. For some types of oil production there are surface disturbances that will result in a loss of biomass and soil carbon. The emissions from these disturbances are included in the coal mining pathway but not in the oil sands pathways. The emissions from this source for oil production pathways have been added to the model where appropriate.

GHGenius has been modified to allow the incorporation of carbon capture and storage (CCS) into many of the fuel and energy pathways in the model. This has been accomplished in a manner that provides a significant amount of flexibility for the user. There is still a considerable amount of uncertainty with respect to the actual performance of CCS systems in real world applications. With some large projects now being proposed some real data may become available in a few years that can be used to further refine the values used in GHGenius.

Tags: Crude Oil - Electricity - Ethanol - Fischer Tropsch - GHGenius 3.3 - Hydrogen - Methanol - Mixed Alcohols - Refining - Sequestration
 GHGenius for India
 Prepared April 2006
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This report documents the changes that have been undertaken to the model to include India as one of the countries capable of being modelled. All of the pathways in the model are capable of being analyzed for India but as described below only a select number of pathways have been updated to include India specific data. The specific deliverables as part of this project include:
1. Undertake a modification to NRCan’s GHGenius model for lifecycle transportation related greenhouse gas emissions so that India is included as a country in the model. The model once updated to include the necessary data and background information on India will be made available in the public domain in India as well as in Canada.
2. The GHGenius modifications for India shall include but not necessarily be limited to updates to the gasoline, diesel and natural gas pathways, for both light-duty and heavy-duty vehicles, and shall consider nationwide and city specific regulatory requirements anticipated to come into effect over a 10-year period commencing April 1, 2005.
3. The GHGenius modifications for India shall include baseline gasoline and CNG light-duty vehicles, baseline diesel and CNG heavy-duty vehicles, and shall provide the ability to model the CNG light-duty vehicles and heavy-duty vehicles.

Most of the required data for the model has been found from public sources. In some cases the data is quite old and the accuracy of the model could be improved with more up to date should it become available.
Preliminary results for light and heavy-duty vehicles are summarized in this report.

Tags: GHGenius 3.2 - India
 Construction Emissions
 Prepared March 2006
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Most analyses of energy production pathways do not include any emissions attributable to the construction of the energy production facilities themselves. This simplification of the production pathway is allowed under ISO 14000 guidelines if the emissions are not material. Many researchers make this claim for the construction and decommissioning stage but there are other analysts who often challenge this perspective. These analysts may use the omission of construction emissions as a reason not to trust a comparison between fuel pathways.

This report documents a literature search of previous work on the emissions associated with the construction of electric power facilities (nuclear, hydro, thermal, and wind), oil refineries, ethanol production plants and other production facilities. The identified literature has been assessed on a common basis and conclusions reached about the GHG emissions from the construction phase of a project. The literature search has identified anther approach to quantifying the emissions from the construction phase of projects, the use of economic input-output data which can be used when the quantification of materials and energy inputs are not available to achieve a reasonable estimate of emissions.

Tags: Biodiesel - Electricity - Ethanol - Hydrogen - Materials - Refining
 FT Distillate Report
 Prepared March 2006
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FT distillates or gas to liquids (GTL) products are becoming commercial fuels in many parts of the world. The plants producing these fuels are likely to be located in regions with stranded gas assets and the products from the plants are likely to be exported all around the world. In GHGenius, the current default conditions indicate that the lifecycle GHG emissions from these fuels are slightly higher than diesel fuel produced from crude oil. There have been other lifecycle analyses performed on the process and some claim a reduction in GHG emissions for this production pathway. Several of these other reports (PwC on the Shell MDS process, the Five Winds International review for Shell, Sasol and Chevron, Energy and Environmental Solutions, LLC report for the US DOE, and the ConocoPhillips and Nexant report) have been reviewed to determine the reasons for the discrepancies.

There are three primary factors that have been identified that contribute to the different results reported for the GHG emission performance of FT distillate fuels. These are the efficiency of the conversion process, the allocation procedure used in the conventional oil refinery for the emissions for individual products, and the emissions associated with natural gas production.

Since all of the reports are relying on engineering studies for the key thermal efficiency value it is not possible to state that one report has used the correct value and another report has used an incorrect value. Different processes configurations will have different efficiencies due to the reforming approach used, the catalysts employed and other factors. Gas composition could also play a role in the overall efficiency. It could also be that different developers provide the data on a different basis, and annual average or steady state operations for example. The start-up and shut down steps can result in significant GHG emissions with little products being produced and these should be amortized over each cycle.

Similarly it is not possible to conclude that one allocation method is superior to another. It can be seen in the PwC and ConocoPhillips work that even using the same functional expansion for co-products very different results can be obtained depending on how the alternative products are produced, natural gas versus coal based products in this case. The choice between natural gas and coal could be different in different regions and may even vary with price, favouring natural gas at low oil prices and coal at high prices. It should be noted that the allocation methodology used in the PwC report would make all alternative fuels look better, not just FT distillates.

There are also differences that will be caused by location differences due to the gas composition, the energy required to produce the gas, and the degree of processing that the raw gas undergoes before entering the GTL plant.

The GHGenius model has been used to quantify the GHG emissions impact of the primary differences between the studies and it has been determined that the process conversion efficiency, the allocation procedures, and the emissions associated with natural gas production are the primary factors leading to the different reported emission results.

Tags: Fischer Tropsch - Natural Gas
 Sugar Cane Ethanol and Other Updates
 Prepared January 2006
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The report covers work on new pathways and updating data. This work involved the following tasks and deliverables.

1. A new table has been added to sheet I that shows the fossil energy consumption per kilometre driven. This augments the existing Table 51c that shows the total energy consumed per kilometre driven.

2. New information on the energy requirements to remove sulphur from gasoline and diesel fuel in Canada has recently become available. This information has been reviewed, and the formulas on sheet G have been changed to be consistent with this new data.

3. There is increased interest in the concept of plug in hybrid vehicles. These vehicles have a larger battery pack than existing hybrid vehicles and must be plugged in to maximize the battery charge. The vehicles operate with some fraction of the energy consumed provided by the power from the grid rather than having all of the power provided by the gasoline engine. These vehicles can operate as a combination of a battery powered electric vehicle and a gasoline vehicle. This pathway has been added to the results sheets using a user specified fraction of distance provided by the grid power and battery size.

4. There has also been interest in modelling gasoline powered heavy-duty vehicles and gasoline hybrid heavy-duty vehicles. These vehicles may also be medium duty vehicles so the classes of vehicles modelled have been expanded by including vehicle weight as part of the user inputs. This will allow the different weight classes to exhibit different efficiencies. The US EPA guidance on this issue has been followed.

5. The production of ethanol from sugar cane has been added to the model. While the production of ethanol from sugar cane is not likely possible in Canada, there has been some sugar cane ethanol imported into Canada and interest in this pathway remains high in Canada. Some information on the production process is available from Brazilian sources and this information has been reviewed to extract the data required for adding this pathway to GHGenius.

6. The energy consumed in the manufacture of materials that are used in the manufacture of vehicles is found on sheet L in the model. This information was briefly reviewed in the 2004 model update but there were still significant gaps in terms of good quality Canadian data availability. This issue has been reviewed again to search for new information that has become available in the past several years on the subject. Updated data has been incorporated into the model.

The version of the model that accompanies this report is version 3.2.

Tags: Ethanol - GHGenius 3.2 - Materials - PHEV - Sugar Cane
 Results from GHGenius
 Prepared January 2006
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This report is intended to provide basic information on GHG emissions for the feedstocks, fuels, energy systems, and materials found in GHGenius to researchers who may want to have a preliminary understanding of the GHG emissions impact of their work. The data presented here is primarily based on the default assumptions for fuel production processes in GHGenius. In many cases the data is presented using multiple functional units, per GJ or per tonne for example. In order to keep the size of this document manageable only very brief explanations are provided of the assumptions and methodology used in GHGenius. Users who wish more detailed information should refer to the GHGenius documentation guide that is available at the reports page of the GHGenius website.

Tags: GHGenius 3.0 - Manual
 Introduction to GHGenius
 Prepared January 2006
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This document provides a brief overview of life cycle assessment methodology, the history, and scope of GHGenius. It identifies the pathways in the model, the data sources and the types of results that the model produces.

Tags: GHGenius 3.0 - Manual
 GHGenius Documentation Manual for Version 3.0
 Prepared September 2005
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This report documents the development of the model and provide the user with an understanding of the primary functions of the model. There is a detailed explanation of each sheet in the model including the data that an advanced user may wish to adjust for a more detailed analysis of a particular fuel pathway. There are references to the latest documentation of the Delucchi LEM and a discussion of where there have been deviations in the development of the two models.

This report also includes instructions on how to run the model and the various options available to the user to provide the user with the desired output.

As part of this work, there have been several changes to the model. The model is now an Excel spreadsheet rather than a Lotus 123 file and the model now uses metric units rather than the mixture of units previously used. The change in units has resulted in more uniformity in how each pathway is modelled. The version of the model that accompanies this report is called GHGenius 3.0. There is information on all of the pathways that have been added to the model in the past two years since the last users guide was prepared.

Tags: GHGenius 3.0 - Manual
 Economic, Financial, Policy Analysis Biofuels, Pha
 Prepared April 2005
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The transportation sector represents the single largest source of Canada's greenhouse gas (GHG) emissions, accounting for about 27% of the total. Emissions from transportation are growing faster than the national average and are forecast to exceed the 1990 levels by over 25% in 2010 and 40% by 2020. Two transportation fuels that are manufactured from biomass feedstocks have been gaining momentum as suitable fuels for use in gasoline and diesel engines, either as neat fuels or in various blends. These fuels are ethanol, manufactured from grains and lignocellulosic feedstocks, and bio-diesel (methyl esters) manufactured from virgin vegetable oils, re-cycled oils, and animal fat.

The 2003 Climate Change Plan for Canada included $154 million to be invested in measures to support Canada's efforts to reduce GHG emissions from transportation. The funds will support the industry to increase the supply of renewable alternative fuels, such as ethanol and bio-diesel, and the commercial transportation sector to make greater use of these fuels.

The Federal Government included a production goal of 35% of Canadian gasoline to be blended with 10% ethanol by 2010 in its Climate Change Action plan. In 2010, this will likely require 1.5 billion litres of ethanol. They have also established a $100 million Ethanol Expansion Program to assist with the construction of new ethanol plants in Canada. The funding under the Ethanol Expansion Program is part of a larger bio-fuels strategy that also includes the extension of the National Biomass Ethanol Program, research and development under the biotechnology component of the Technology and Innovation Strategy and an investment in bio-diesel.

The Federal Government has included a production goal of 500 million litres of biodiesel by 2010 in its Climate Change Action plan. They have also established an $11.9 million fund that will support research and provide incentives for industrial-scale biodiesel pilot plants, and support demonstrations of its effectiveness to encourage broader use of biodiesel.
In addition to encouraging increased production, the Government of Canada is also promoting greater use of ethanol and biodiesel. In partnership with several gasoline retailers, the Government of Canada is launching a consumer awareness campaign that will promote the benefits of ethanol-blended gasoline to Canadian drivers. There are currently more than 1,000 retail locations selling ethanol-blended gasoline in Canada.
There has been little economic and financial analysis of these fuels within a Canadian context. The few published and unpublished studies carried-out so far for the public sectors have dealt mostly with potential socio-economic impacts and have attracted little interest from the investment community due to their lack of focus on profitability, both short and long term. More detailed feasibility studies have been performed for individual private sector clients but these have not been widely disseminated. Policy and decision makers, financial institutions, and other economic players need the more detailed, formal analysis framework in order to make investment decisions regarding the development of these fuels.

Some essential topics that will be addressed in this work are:
·The appropriate policy and regulatory environment under which investments will flow into ethanol plants;
·The likely source of these investments;
·And the industry structure that will lead to a viable and competitive industry in the longer term.

Much work remains to be done in this area to establish a purely Canadian perspective, if Canada is to entertain the notion of building a bio-based economy as part of its future.

The development of a biofuel industry will require a great deal of investment on behalf of fuel suppliers, fuel marketers, and many levels of governments. The federal government's role will be to encourage the development of the biofuel industry through the implementation of sensible regulatory and policy tools based on sound analytical work. This work will form a foundation for the development of those tools.

Objective and Approach

The primary objective of this study is to assess the current and future economics of ethanol plants in Canada and to develop estimates of demand, supply, and prices (costs and selling) of this fuel. The results are then used to develop a template-like analytical tool for various models of ownership structure, to help assess the financial performance of various types of fuel ethanol (regional and feedstock specific) plants across Canada.

The work was carried out in Phases and stages. This report covers Phase 2, for fuel ethanol and biodiesel.
Phase 2 of the work focuses on quantifying the effects of ethanol and biodiesel production and use from a full cost accounting perspective. The work includes:
·A literature review of existing full cost accounting studies on biofuels.
·Descriptions of the relative benefits and costs of biofuel production within the context of greenhouse gas emissions, air quality, safety risk, employment and tax benefits and resource use.
·Identification of case studies that would optimize the benefits from a full cost accounting perspective.
·Identification of the gaps in the existing understanding of full cost accounting and how they might be addressed in the future.

Tags: Biodiesel - Economic - Ethanol
 Ethanol GHG Emissions Update
 Prepared March 2005
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The primary focus of this work is to update the data, on the materials and energy inputs into the ethanol production processes and co-products produced, used for modelling the lifecycle emissions of the ethanol pathways. Lifecycle emission modelling results are only as good as the quality of the input data so it is important to review this data on a regular basis.

The primary work on the ethanol pathways in GHGenius was undertaken in 1999 and while there have been some minor updates since then, the primary data in the model has not been reviewed in the past five years. There are several important issues that have been investigated and added to the model to better reflect the emissions of modern plants. These include:
1. Reviewed and updated energy requirements for grain ethanol plants. There have been significant reductions in the energy requirements of new grain ethanol plants in the past five years. This progress has been reviewed and incorporated into the model.
2. The addition of the capture and liquefaction of carbon dioxide. Ethanol plants produce a very concentrated carbon dioxide stream, which can be captured and liquefied for use in a variety of industrial applications. Alternative sources of carbon dioxide are less concentrated and require more energy to concentrate and purify. Depending on the degree to which carbon dioxide from ethanol plants displace carbon dioxide from other sources there can be an energy and emissions credit applied to the ethanol plant. This alternative processing scheme has been added to the model with the flexibility to activate or not, either fully or partially.
3. In the development of commercial ethanol from lignocellulose plants there are new co-products being developed. Some of these include fertilizers and soil conditioners. The model has been expanded to include fertilizer co-products.
4. There is some information in the literature that suggests that some animals that consume distillers dried grains have lower levels of flatulence. The literature has been surveyed for further information on this issue and this emission credit has been incorporated into the model.

Updated Data

A major part of any life cycle analysis is the collection of the data on the inputs and outputs of the production cycle being analyzed. The quality of the data has a large impact on the quality of the results being calculated. Data quality must balance the available time and resources against the quality of the data required to make a decision regarding overall environmental or human health impact.
In GHGenius the data on many of the production pathways is continually being updated as new information sources emerge or as processes evolve. Recently new information on the emissions of the fertilizer sector in Canada has become available, and new information on nitrous oxide emissions in the agriculture sector are also available. These changes to the model were described in a recent report on the emissions from biodiesel production ((S&T)2, 2005). These changes also impact the ethanol fuel pathways either directly in the case of fertilizer or indirectly in the case of soybean emissions since the distillers dried grains (DDG) displaces soybean meal and the emissions credit for the DDG is a function of the emissions from the soybean lifecycle.
The input variables are another class of data input. These variables can be expected to differ in different regions of the country or in different countries in response to different practices or environmental conditions. These inputs are generally found on the input sheet in GHGenius as one expects them to be changed in different circumstances.
These input variables for the different ethanol pathways have been reviewed and where better data now exists for some variables, this information has been added to GHGenius. It is important to keep in mind that the general approach to the data in GHGenius is to model the most likely scenarios, not the best-case scenario where practices or existing equipment would have to change to produce the modelled results.

Co-Product Additions

Several new co-products have been added to GHGenius that are applicable to the ethanol pathways. The ability to model the capture of carbon dioxide and have that product displace gas from alternative production sources in now available. Cellulosic ethanol production processes produce large amounts of lignin, which can be processed by different means. It is possible to burn the material and produce electricity or it may be possible to sell the material for other applications. One application may be as a source of fertilizer and this capability has been added to the model. The handling of other co-products such as acetic acid has been improved in the model.
The co-product credits for DDG have been reviewed. The displacement factors for DDG have been reduced as more feed trials suggest lower displacement ratios that are used in other models and have been used in previous versions of GHGenius. The value of DDG as animal feed is a very complicated subject as there are many components in a typical ration and it is almost impossible to design a reasonably sized experiment to isolate the impact of one of the components. There is a significant amount of information on the impact of diets on methane production from cattle. It is clear that introducing dietary supplements, particularly ones with by-pass protein will reduce the methane production rate in dairy and beef cattle. Estimates of the impact of DDG on methane emission rates have been made and incorporated into the model.

Tags: Corn - Ethanol - GHGenius 2.6 - Lignocellulosic - Wheat
 Cost Effectiveness Methodology and Results
 Prepared March 2005
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The results of the emission analysis of the various fuel pathways provides useful information for the comparison of pathways but it does not provide any information about the cost of one pathway versus another. It is important not only to be able to reduce emissions from the transportation sector but to do so in a cost effective manner. The function of the cost effectiveness calculations in GHGenius is therefore to integrate information on the relative costs of alternative fuels and vehicles with the emissions results produced by the model to arrive at the cost of greenhouse gas emission reductions achieved by the pathway.
The goal of this work was to enhance the functionality of the cost effectiveness calculations. The specific actions undertaken to achieve this were:
1. In version 2.5 of the GHGenius model, fuel costs and operating and maintenance costs were discounted but at different rates. The cost effectiveness is then discounted as well. The discounting of the fuel costs and the operating and maintenance costs have been removed and now the model just discounts the cost effectiveness.
2. There were a number of intermediate calculation results shown on the cost effectiveness sheets that did not add any useful information. These have been removed to simplify the presentation. The calculations in the final result cells have been changed to draw the data from other locations in the model rather from these intermediate result locations.
3. The lifetime vehicle mileage was chosen automatically based on average mileage accumulation rates and the economic life of the vehicle. This has been changed to a user specified value so that special cases such as high mileage vehicles can be modelled. Some of these high mileage vehicles should offer the most cost effective GHG reduction opportunities.
4. The cost effectiveness calculations were designed on a pre-tax basis. This was done to facilitate the comparison of pathways on a purely fuel and vehicle cost basis. This is done because many alternative fuels currently benefit from excise tax and in some cases provincial tax exemptions. In some cases however, where there is an incremental vehicle cost but gasoline or diesel fuel is still used, it is important to consider the implication of fuel taxes in the calculation. The provision for adding fuel taxes to the cost determination was included so that the model can calculate the cost effectiveness from both perspectives.
5. The fuel costs for crude oil derived fuels is now calculated from a user set crude oil price. This allows for rapid analysis of the impact of changing oil price scenarios.
6. There are market relationships between crude oil and its primary products, natural gas, propane and the fuels made from them. A means of linking all of these fuels back to the price of crude oil was explored. Data was collected and analyzed on the recent historical relationships between these products.
7. While it was not possible to link all of the fossil fuels together, it was possible to create some “caution messages” for the user. For example, hydrogen from the SMR pathway could have the feedstock calculated by the model from the price of natural gas and this cost could be compared to the user input value. This could not be done for every fuel but it has been done for a number of them.
8. There is much more information in existence today concerning the production economics of many of the alternative fuels than there was in 1999. Improved estimates of this data should be available in the near future. The information from the recent work on biofuels should be able to be used for the prices of ethanol and biodiesel.

Tags: Economic
 Biodiesel GHG Emissions Update
 Prepared January 2005
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The production of biodiesel from vegetable oils, tallow and yellow grease was added to GHGenius in 2002. In 2004 the production and conversion of marine oils to biodiesel was added to the model. Since 2002, there has been growing interest in the production of biodiesel in Canada. The National Research Council has recently completed a lifecycle analysis of biodiesel and that effort produced additional data that is applicable to biodiesel production. A number of new European LCA reports are now available and these have additional data that can be incorporated into the analysis. There have been a number of comments and suggestions made with respect to the original 2002 report so it is appropriate to revisit the input data for these pathways in GHGenius. Additional information has also recently become available on fertilizer production in Canada so that work has also been reviewed and incorporated into the model.

The original work in 2002 included an assessment of ethanol-diesel blends. Those fuels are not included in this work although a review of the emissions from ethanol production and ethanol blends is planned for the near future.
The goal of this work is to:
· Expand the biodiesel pathways in the model so that tallow and yellow grease pathways can be analyzed at the same time rather than have them share a pathway where the user must select which to model,
· Add the intermediate production of the lipid feedstock to the upstream results on Sheet K,
· Regionalize the production of fertilizer in the model,
· Review and update the data that is used in the biodiesel production pathways,
· Review and discuss the role of the biodiesel co-products in the LCA, and to
· Use the model to perform some sensitivity analysis on the inputs in the biodiesel pathways so that a better understanding of biodiesel’s benefits can be achieved.

GHGenius has been expanded to include five biodiesel pathways and all five are available for each model run. In addition, the upstream emissions are available for the five oils used as biodiesel feedstocks.

Tags: Biodiesel - Canola - Fertilizer - GHGenius 2.6 - Marine Oil - Soybeans - Tallow - Yellow Grease
 Light Duty Vehicle Exhaust Emissions Update
 Prepared December 2004
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GHGenius projects the average emissions for a single model year vehicle over its lifetime. The primary emissions model (MOBILE) used for projecting exhaust emissions of vehicles projects the emissions in a single year of the fleet of vehicles. The two models therefore look at the emissions issue from different perspectives. There is a relationship between the two approaches and GHGenius can use the raw data from MOBILE as the basis for its emission projections.

The emissions of the criteria air contaminants from light duty vehicles in GHGenius were originally calculated using an algorithm that was based on the US EPA MOBILE5 emissions model. In 2001, the EPA replaced this model with a new model called MOBILE6. The light duty emissions in GHGenius were updated in 2001 when early versions of MOBILE6 first became available. This model has now been superseded by MOBILE6.2 in the United States. Environment Canada has a similar model called MOBILE6.2C, which has some Canadian data in it. The updated emissions models not only have a better understanding of on-road emissions, they incorporate the known future changes in emissions regulations into their projections for emissions.

Emissions forecasts from light duty onroad vehicles were calculated using the MOBILE6.2C emission factor model developed by the US Environmental Protection Agency and subsequently adapted to Canadian vehicles by Environment Canada. This is the most current model available for calculating in-use emissions from light and heavy duty motor vehicles and takes into account:
· the year of evaluation and the model year and applicable emission standards for vehicles in a fleet,
· effects of key fuel quality parameters on emissions, i.e., fuel sulphur content for diesel fuel and gasoline, and vapour pressure and general chemical composition for gasoline,
· climatic conditions, such as ambient temperature,
· results of a large body of work done to quantify emissions from light and heavy motor vehicles, such as US federal test procedure emission tests, off-cycle emission tests and in-use emission tests,
· effects on emissions of vehicle duty cycles and operation on different types of roads (freeway, freeway ramp, arterial and local roads).
MOBILE is typically run for large fleets in a single year, rather than a single vehicle over its lifetime, like in the GHGenius model. A database of MOBILE6.2C results was obtained that contained output data for a single model year every three years from 1987 to 2017. Additional years were run to fill in data when there were large changes in the emissions over the three year period. The results were analyzed for light duty gasoline vehicles, light duty diesel vehicles and natural gas powered light duty vehicles.

These results from MOBILE 6.2C were then programmed into GHGenius.

Tags: Exhaust Emissions
 Economic, Financial, Policy Analysis Ethanol
 Prepared November 2004
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The Federal Government included a production goal of 35% of Canadian gasoline to be blended with 10% ethanol by 2010 in its Climate Change Action plan. In 2010, this will likely require 1.5 billion litres of ethanol.

They have also established a $100 million Ethanol Expansion Program to assist with the construction of new ethanol plants in Canada. The funding under the Ethanol Expansion Program is part of a larger bio-fuels strategy that also includes the extension of the National Biomass Ethanol Program, research and development under the biotechnology component of the Technology and Innovation Strategy and an investment in bio-diesel.

There has been little economic and financial analysis of ethanol within a Canadian context. The few published and unpublished studies carried-out so far for the public sectors have dealt mostly with potential socio-economic impacts and have attracted little interest from the investment community due to their lack of focus on profitability, both short and long term. Policy and decision makers, financial institutions, and other economic players need the more detailed, formal analysis framework in order to make investment decisions regarding the development of these fuels.

The primary objective of this study is to assess the current and future economics of ethanol plants in Canada and to develop estimates of demand, supply, and prices (costs and selling) of this fuel. The results are then used to develop a template-like analytical tool for various models of ownership structure, to help assess the financial performance of various types of fuel ethanol (regional and feedstock specific) plants across Canada.

The work was carried out in Phases and stages. This report covers Phase 1, for fuel ethanol. A similar report has been prepared for biodiesel.

The specific objectives of Phase 1, Stage 1 were to:
· Review literature on economic and financial performance of ethanol plants.
· Identify successful plants and reasons for success.
· Quantify feedstock resources and production costs.
· Develop a comprehensive financial model.
· Develop a supply curve.

The objectives of Phase 1, Stage 2 were to:
· Identification of market barriers.
· Evaluate policy tools including.
o Government capital investment
o Favourable tax treatment
o Infrastructure investment
o R&D funding
o Renewable content mandates
o Emission taxes
· Examine the potential for regionalization of tools.
· Quantification of levels of support required.
· Investigate other approaches to market development.

Phase 1, Stage 3 of the work focuses on the international aspects of a developing ethanol industry and considers the threats and opportunities that international trade in biofuels presents. The specific tasks of this stage include:
· Identification of the level of international trade.
· Production cost comparison with the potential exporters of fuel ethanol.
· Analysis of the import alternatives that ethanol users in Canada would face.
· Evaluate the impacts that ethanol imports might face and identify measures that might mitigate the impacts.
· Evaluate the impact of trade agreements on enabling or disabling Canadian industry competitiveness.

The next phase of the work will include some GHG analyses.

Tags: Corn - Economic - Ethanol - Lignocellulosic - Wheat
 Economic, Financial, Policy Analysis Biodiesel
 Prepared November 2004
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The Federal Government has included a production goal of 500 million litres of biodiesel by 2010 in its Climate Change Action plan. They have also established an $11.9 million fund that will support research and provide incentives for industrial-scale biodiesel pilot plants, and support demonstrations of its effectiveness to encourage broader use of biodiesel.

There has been little economic and financial analysis of biodiesel within a Canadian context. The few published and unpublished studies carried-out so far for the public sectors have dealt mostly with potential socio-economic impacts and have attracted little interest from the investment community due to their lack of focus on profitability, both short and long term. Policy and decision makers, financial institutions, and other economic players need the more detailed, formal analysis framework in order to make investment decisions regarding the development of these fuels.

The primary objective of this study is to assess the current and future economics of bio-diesel plants in Canada and to develop estimates of demand, supply, and prices (costs and selling) of this fuel. The results are then used to develop a template-like analytical tool for various models of ownership structure, to help assess the financial performance of various types of biodiesel (regional and feedstock specific) plants across Canada.

The work was carried out in Phases and stages. This report covers Phase 1, for biodiesel. A similar report has been prepared for ethanol.

The specific objectives of Phase 1, Stage 1 were to:
· Review literature on economic and financial performance of biodiesel plants.
· Identify successful plants and reasons for success.
· Quantify feedstock resources and production costs.
· Develop a comprehensive financial model.
· Develop a supply curve.

The objectives of Phase 1, Stage 2 were to:
· Identification of market barriers.
· Evaluate policy tools including.
o Government capital investment
o Favourable tax treatment
o Infrastructure investment
o R&D funding
o Renewable content mandates
o Emission taxes
· Examine the potential for regionalization of tools.
· Quantification of levels of support required.
· Investigate other approaches to market development.

Phase 1, Stage 3 of the work focuses on the international aspects of a developing biodiesel industry and considers the threats and opportunities that international trade in biofuels presents. The specific tasks of this stage include:
· Identification of the level of international trade.
· Production cost comparison with the potential exporters of biodiesel.
· Analysis of the import alternatives that biodiesel users in Canada would face.
· Evaluate the impacts that biodiesel imports might face and identify measures that might mitigate the impacts.
· Evaluate the impact of trade agreements on enabling or disabling Canadian industry competitiveness.

The second phase of the work will have some analysis related to GHG emissions.

Tags: Biodiesel - Canola - Economic - Marine Oil - Soybeans - Tallow - Yellow Grease
 Marine Based Biodiesel
 Prepared November 2004
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GHGenius has had pathways for the production of biodiesel from canola, soybeans, animal tallow and yellow grease. This work adds the generic production of biodiesel from marine oils to GHGenius and compares those results to a specific Canadian operation, Ocean Nutrition, producing marine oil biodiesel.
Ocean Nutrition produces an ethyl ester from marine oils in Nova Scotia. The process is commercially unique from several perspectives including, the use of ethanol rather than the more common methanol as the alcohol, the use of marine oils as the feedstock, and the co-processing that is carried out to produce Omega 3 oils for nutritional purposes as well as producing biodiesel.
The goal of this work is to:
· Add the commercial harvesting of fish and its reduction to proteins and oils to GHGenius.
· Add a biodiesel pathway that utilizes marine oils as the feedstock to complement the existing animal fats and vegetable oil pathways.
· Modify and expand GHGenius to allow the use of ethanol rather than methanol in the biodiesel production system. Review the literature to determine how others have addressed this issue since the carbon in the ethanol is renewable whereas in the methanol it is not. It may be that the impact is on the glycerine production and how that is ultimately used.
· Address the allocation issues raised by the co-production of biodiesel and the high value Omega-3 oils produced in the Ocean Nutrition process.

Tags: Biodiesel - Ethanol - GHGenius 2.5 - Marine Oil - Methanol
 Alternative and Future Fuels for Road Vehicles
 Prepared for Transportation Issues Table, National Climate Change Process in July 1999
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The first work that used the Canadian version of LEM and formed the basis for the development of GHgenius.
Considered Light and Heavy Duty Vehicles
14 fuels considered.
Greenhouse gas emission reductions calculated.
Cost effectiveness of GHG reductions calculated.

Tags: Ethanol - Hydrogen - Methanol - Natural Gas
 GHG Emissions from Natural Gas Vehicles
 Prepared for Canadian Natural Gas Vehicle Alliance in January 2003
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The objectives of this work were to document the benefits of the use of natural gas as a vehicle fuel in three classes of vehicles. The vehicles classes that were of interest were:
  • Light duty vehicles using compressed natural gas, with a focus on full size passenger cars and vans.
  • Medium duty vehicles that could be fuelled with either compressed or liquefied natural gas. These might be refuse haulers or urban buses.
  • Heavy-duty trucks that use liquefied natural gas and the Westport Cycle engines.

For each of these classes of vehicles the impact that the fuel and engine has on greenhouse gas emissions, on the cost effectiveness of the greenhouse gas emission reduction and on criteria air contaminants were calculated and reported.

Tags: GHGenius 2.1 - Natural Gas
 Addition du Charbon et de la Biomassse aux Procede
 Août 2003
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Ce travail a pour but d'ajouter deux nouveaux procédés de production d'hydrogène, soit à partir du charbon et à partir de la biomasse. Les nouveaux procédés de production sont entièrement intégrés dans GHGenius; pour chaque cycle de carburant, le carburant est utilisé dans des applications peu intenses et intenses faisant appel à des piles à combustible.

Ces deux nouveaux procédés de production vont probablement nécessiter de grandes usines. L'hydrogène sera transporté depuis ces usines jusqu'aux endroits où il sera distribué. Les versions précédentes de GHGenius traitaient le transport de l'hydrogène (comprimé ou liquide) comme s'il s'agissait d'un combustible liquide. Cela a été changé dans la nouvelle version de GHGenius. Les facteurs d'énergie consommée sont spécifiques et différents pour l'hydrogène liquide et pour l'hydrogène comprimé. L'utilisateur a maintenant une plus grande flexibilité pour modéliser la distribution de l'hydrogène.

Préparé pour le compte de : Ressources naturelles Canad
 Hydrogen Pathways, Greenhouse Gas Emissions and En
 Prepared for Fuel Cells Canada and Natural Resources Canada in December 2003
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Fifty pathways for transportation fuels are evaluated for their lifecycle greenhouse gas emissions. Forty-five of those involve hydrogen. Thirty-six pathways have been investigated for their energy use and thirty-one of those involve hydrogen. The hydrogen pathways that are studied include the following components:
  • Feedstocks. The following feedstocks can be converted to hydrogen: coal, crude oil, natural gas, biomass, nuclear energy, and hydropower (can also be used as a proxy for wind and solar).
  • Intermediate Products. In addition to the direct production of hydrogen, some of the feedstocks mentioned above can produce various intermediate energy carriers that can be used for the eventual production of hydrogen; these include methanol, electricity, ethanol, LPG, FT Distillate, and gasoline.
  • Distribution. Hydrogen can be produced on site or it can be produced at a central facility. The distribution from a central facility can be as a liquid or a compressed gas. The compressed gas can be distributed by pipeline or by truck. Liquid hydrogen can be distributed by truck or rail. Some of the pathways will only be feasible with large central facilities that require hydrogen distribution while others could be small decentralized systems or large central systems. The impacts of the distribution system on the results are discussed and the most likely option for each pathway can be modeled.
  • Utilization. The hydrogen could be used in an internal combustion engine or in a fuel cell. The data in GHGenius for the hydrogen ICE has been reviewed with a literature search to ensure that it is consistent with the latest developments in this area.


Tags: Coal - Crude Oil - Fuel Cell - GHGenius 2.3 - Hydrogen - Hydrogen Transportation - Natural Gas
 Update of GHGenius
 Prepared for Natural Resources Canada in March 2004
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As part of this work, there have been several changes to the model. The version of the model that accompanies this report is GHGenius 2.5. A number of revisions and updates to the model were undertaken. The revisions generally have either improved the quality of Canadian data in the model or added functionality that makes the model more powerful for the user. In addition, two new cycles, based on processes developed by NRCan have been added to the model. These new cycles are the subject of a separate report. The objectives of the model updates were to:
  • Allow selection of ethanol cellulosic feedstock from the input sheet. Ethanol from lignocellulosic feedstocks could be produced from a variety of feedstocks including wheat straw, corn stover, switchgrass, and hay.
  • Improve the quality and functionality of the emissions from crude oil production and refining in Canada. The model has been modified to use four different crude oil types, conventional, heavy, bitumen and synthetic.
  • Harmonize the methodology for the production of hydrogen from electrolysis with other methods.
  • Review and update the Canadian electricity mix.
  • Update the emissions of criteria air contaminants from heavy-duty diesel engines on Sheet H in light of the new emission standards being phased in this decade.
  • The latest LEM model by Dr. Mark Delucchi made many changes to sheet L that calculates emissions associated with materials. The documentation that describes the changes to determine the best data to use for GHGenius with particular attention to the data for Canada has been reviewed and the appropriate changes have been made to GHGenius.


Tags: Crude Oil - Electricity - Ethanol - Exhaust Emissions - GHGenius 2.5 - Lignocellulosic - Refining
 NRCan SuperCetane and Used Oil Cycles
 Prepared for Natural Resources Canada in March 2004
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The goal of this work was to add two new pathways to GHGenius:
  • Canola Oil or tallow to “SuperCetane”, and
  • Used motor oil to diesel fuel, the ROBYS™ process.

Both of these processes have been developed by Natural Resources Canada and are in the process of being commercialized.
The new pathways have been fully integrated into GHGenius and all of the existing functionality of the model has been retained.
The CANMET Energy Technology Centre (CETC), Natural Resources Canada, has developed a novel, patented technology that can convert vegetable oils, waste greases, animal tallow and other feedstocks containing triglycerides and fatty acids into a high cetane, low sulphur diesel fuel blending stock called SuperCetane. This process can transform fats by hydrotreating them to produce paraffins.
The "ROBYS™ Process" purifies and stabilizes reclaimed and refined gas oils. ROBYS™ is designed as an add-on unit to used oil recycling and petroleum refining operations. The process was developed by the CANMET Energy Technology Centre (CETC) and is licensed to Par Excellence Developments (PED) of Sudbury, Ontario for worldwide application. In the course of being recycled, used oils undergo a thermal cracking process to produce gas oil. ROBYS™ then effectively stabilizes and purifies the gas oil.
Used oil has been added to GHGenius as a feedstock. The collection parameters for used oil can be set by the user on the Input Sheet in terms of the modes of transportation employed and the distances involved.

Tags: Canola - GHGenius 2.5 - SuperCetane - Tallow - Used Oil
 Biomass to Syngas Processes
 Prepared for Natural Resources Canada in March 2004
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The goal of this work was to add five new pathways to GHGenius. The new pathways are:
  • Wood to mixed alcohols,
  • Refuse derived fuel (RDF) to mixed alcohols,
  • Wood to FT distillate,
  • RDF to FT distillate, and
  • Natural gas to mixed alcohols.


The new pathways are fully integrated into GHGenius, for each fuel cycle the fuel is used for both light duty and heavy duty applications. All of the existing functionality of the model has been retained.
This work has involved the development of a new feedstock, RDF. This material is produced by collecting, separating and in some cases processing municipal solid waste (MSW). The user can now specify all of the key parameters, collection distances, processing energy, and material yield for collecting and converting MSW to RDF.
A new fuel, mixed alcohols has been added to the model. This fuel is a mixture of C1 to C5 alcohols and the user can specify the mixture of alcohols consistent with the rest of the inputs. This fuel could be used in low level blends with gasoline or diesel fuel or as a fuel itself in large heavy-duty engines. All of these applications have been added to the model.
While the primary interest in the mixed alcohols is for their production from the renewable feedstocks, wood and RDF, a pathway to produce the mixed alcohols from natural gas has been added for comparison.
It is also feasible to produce FT Distillate from wood or RDF rather than from natural gas and these are the fourth and fifth pathways added to the model, The FT Distillate is used in heavy-duty engines either alone or in a blend with conventional diesel fuel. The production of FT Distillate from natural gas was already in the model for comparison.

Tags: Fischer Tropsch - GHGenius 2.4 - Mixed Alcohols - Municipal Solid Waste - Natural Gas - RDF - Wood
 Coal and Biomass to Hydrogen
 Prepared for Natural Resources Canada in August 2003
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The goal of this work was to add two new hydrogen pathways, coal to hydrogen and biomass to hydrogen. The new pathways are fully integrated into GHGenius, for each fuel cycle the fuel will be used for both light duty and heavy duty applications fuel cell applications. All of the existing functionality of the model has be retained.
Both of these new pathways are likely to involve large individual plants. The hydrogen will be transported from these plants to the locations where it will be dispensed. Previous versions of GHGenius handled the transportation of hydrogen in different and less robust ways than the distribution of other fuels. This has been changed in this new version of GHGenius. The user now has much greater flexibility to model the way that hydrogen is distributed.

Tags: Coal - GHGenius 2.3 - Hydrogen - Hydrogen Transportation - Wood
 GHGenius Documentation Manual
 Prepared for Natural Resources Canada in May 2003
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This report documents the development of the model and provides the user with an understanding of the primary functions of the model. There is a detailed explanation of each sheet in the model including the data that an advanced user may wish to adjust for a more detailed analysis of a particular fuel pathway. There are references to the latest documentation of the Delucchi LEM and a discussion of where there have been deviations in the development of the two models.
This report also includes instructions on how to run the model and the various options available to the user to provide the user with the desired output.
As part of this work, there have been several changes to the model. The version of the model that accompanies this report is called GHGenius 2.2. It now includes some outputs for emissions related to space heating.

Tags: GHGenius 2.2 - Manual
 Landfill Gas to Methanol
 Prepared for Natural Resources Canada in January 2003
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An interesting pathway to produce transportation fuel is to process landfill gas to methanol. Landfill gas typically contains 50-60% methane, which is a powerful greenhouse gas. The methane can be captured and then flared or used in an energy recovery process. There are a number of landfills, including some in Canada, which use the captured gas to produce steam and electricity. An alternative process is to convert the methane into a transportation fuel such as methanol. The methanol can be used in spark-ignited engines (M85), in modified compression ignited engines (M100), reformed onboard for use in fuel cell vehicles, or reformed to produce hydrogen for use in fuel ell vehicles.
This fuel cycle that uses landfill gas to produce methanol has been successfully added to GHGenius. The model has also expanded the use of hydrogen from electrolysis by adding a heavy-duty fuel cell pathway. Each of the new pathways has full functionality in the model including the summary sheets and the cost effectiveness calculations.

Tags: Electrolysis - GHGenius 2.1 - Hydrogen - LFG - Methanol
 Ethanol Production from Wheat
 Prepared for Natural Resources Canada in January 2003
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The production of ethanol from wheat is practiced commercially on a small scale in Western Canada. In addition to the Federal goal of increasing ethanol production, there are also proposals and actions to significantly expand ethanol production in Saskatchewan and Manitoba. The objectives of this work were to:
  • Add the wheat to ethanol upstream fuel cycle to the GHGenius model. The ethanol that is produced could be used wherever ethanol from corn is used so all of those full fuel cycles were added to the model.
  • While the GHGenius model calculates the greenhouse gas emission credits for co-products, the calculation of energy credits for the co-products is not currently included in the model. This improved functionality has been added to the model.


Tags: Ethanol - GHGenius 2.1 - Wheat
 Biodiesel and Ethanol Diesel Blends
 Prepared for Natural Resources Canada in September 2002
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This report accomplished the following objectives:
  • Determined the lifecycle energy balance and greenhouse gas emissions for biodiesel manufactured from waste animal fats, soyoil and Canola oil.
  • Determined the impact on greenhouse gas emissions of using biodiesel blends of 2%, 20% and 100% compared to conventional diesel fuel
  • Determined the impact on greenhouse gas emissions of using ethanol diesel blends of 7% and 15% compared to conventional diesel fuel.
  • Confirmed the appropriate treatment of N2O emissions from agricultural residues in the latest version of GHGenius.
  • Estimated the production costs of biodiesel from the various feedstocks studied.
  • Provided an overview of the policy issues raised by the blending of biodiesel or ethanol diesel blends. These include taxation issues, impact on exhaust emissions, regulatory issues with respect to safety and standards, engine warranty issues and distribution and marketing issues.


Tags: Biodiesel - Canola - Ethanol - Fertilizer - GHGenius 2.0 - Soybeans - Tallow - Yellow Grease
 Off Board Generation of Hydrogen for Fuel Cell Veh
 Prepared for Natural Resources Canada in August 2002
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The purpose of this work was to add fuel cycles to GHGenius that may be demonstrated in the Canadian Transportation Fuel Cell Alliance demonstrations and allow an assessment of the projected greenhouse gas benefits before the projects are funded by the CTFCA.
The GHGenius model has been successfully updated with additional hydrogen production and hydrogen utilization pathways. The following hydrogen production pathways have been added:
  • Off board reforming of methanol
  • Off board reforming of ethanol
  • Off board reforming of gasoline
  • Off board reforming of FT Distillate
  • Off board reforming of LPG
  • The use of nuclear energy to produce hydrogen through thermal cracking

In addition, the use of mixtures of natural gas and hydrogen (Hythane®) in both light duty spark ignited engines and in heavy-duty natural gas engines have been added to the model. The hydrogen for these mixtures can be produced either from SMR or from electrolysis.

Tags: Fischer Tropsch - Fuel Cell - Gasoline - Hydrogen - Hythane - Natural Gas - Nuclear Thermo Cracking
 GHG Emissions from Fuel Cell Vehicles
 Prepared for Methanex Corporation in June 2000
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The primary intent of this report is to cover most of the fuels currently being considered for FCV and to determine the GHG emissions in the Canadian context. GHGenius was used to calculate GHG’s and is capable of calculating emissions in Canada and the United States so the results for the United States are also presented. There is some discussion of the likely results in Japan and Europe based on the carbon intensity of their electricity generating sectors.

Tags: Fischer Tropsch - Fuel Cell - Hydrogen - Methanol - Natural Gas
 Ethanol from Lignocellulosic Feedstocks
 Prepared for Agriculture and Agri-Food Canada in December 1999
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This study was undertaken to provide an analysis of the life-cycle emissions and life-cycle energy balance of the production of ethanol from several agricultural lignocellulosic feedstocks and its subsequent use as a motor fuel in blends with gasoline. The study focuses specifically on Southern Ontario, an area with a large agricultural land base, as well as one with a large demand for motor gasoline. Energy and emission analysis was conducted in this study for a base case ethanol production volume of 225 ML per year in 2000 and 2010. Further analysis was done to investigate the effects of annual ethanol production volumes of 500 ML, 750 ML and 1,000ML. The analyses were performed for four feedstocks, switchgrass, hay, corn stover and wheat straw.

Tags: Ethanol - Lignocellulosic
 Ethanol from Corn in Southern Ontario
 Prepared for Agriculture and Agri-Food Canada in August 1999
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This study was undertaken to provide an analysis of the life-cycle emissions and life-cycle energy balance of the production of ethanol from corn and its subsequent use as a motor fuel in blends with gasoline. The study focuses specifically on Southern Ontario, which is the largest corn growing area in Canada, as well as one with a large demand for motor gasoline. Energy and emission analysis was conducted in this study for a base case ethanol production volume of 225 ML per year in 2000 and 2010. Further analysis was done to investigate the effects of annual ethanol production volumes of 500 ML, 750 ML and 1,000ML.

Tags: Corn - Ethanol
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